Robert Coyle

7/23/2019 Robert Coyle

http://slidepdf.com/reader/full/robert-coyle 1/91

National College of Ireland

Higher Diploma in Science in Data Analytics

2013/2014

Robert Coyle

X13109278

[email protected]

The Use of Twitter Activity as a Stock Market

Predictor



The Use of Twitter Activity as a Stock Market Predictor 2

Table of Contents

ABSTRACT ........................................................................................................................................... 6

DEFINITIONS, ACRONYMS, AND ABBREVIATIONS ................................................................ 6

INTRODUCTION ................................................................................................................................. 7

RELATED WORK ................................................................................................................................ 8

SYSTEMS AND DATASETS .............................................................................................................. 8 DESIGN AND ARCHITECTURE ......................................................................................................................... 8

Brief description of work carried out .................................................................................................... 8 DATASETS .......................................................................................................................................................... 8

Gathering of Twitter Data. ......................................................................................................................... 9

Gathering of Stock Price Data ................................................................................................................ 15 Data Preparation ......................................................................................................................................... 16

REQUIREMENTS ............................................................................................................................................. 17 Data requirements ....................................................................................................................................... 17 User requirements ............... ................ ................ ................ ............... ................. ............... ................ .......... 17 Usability requirements............................................................................................................................... 17 Functional Requirements ......................................................................................................................... 17

TESTING AND EVALUATION ........................................................................................................19 SYSTEMS TESTING. ........................................................................................................................................ 19

Apple Stock ...................................................................................................................................................... 19 Microsoft Stock .............................................................................................................................................. 25

Tesla Stock .............. ................ ................ ................ ............... ................ ................ ................. ............... .......... 33 FORMULA FOR PREDICTING STOCK MOVEMENT ..................................................................................... 36

Formula Used ................................................................................................................................................. 36 Apple Stock Prediction ............................................................................................................................... 36 Microsoft Stock Prediction ....................................................................................................................... 40 Tesla Stock Prediction ................................................................................................................................ 43

CONCLUSION .....................................................................................................................................46

FURTHER DEVELOPMENT ...........................................................................................................47

BIBLIOGRAPHY ................................................................................................................................48

APPENDIX ..........................................................................................................................................48 Project Materials: ......................................................................................................................................... 48

PROJECT PROPOSAL ......................................................................................................................49 INTRODUCTION .............................................................................................................................................. 49 BACKGROUND ................................................................................................................................................ 49 TECHNICAL APPROACH ................................................................................................................................ 50 SPECIAL RESOURCES REQUIRED ................................................................................................................. 50 PROJECT PLAN ............................................................................................................................................... 51 TECHNICAL DETAILS .................................................................................................................................... 51 SYSTEMS/DATASETS .................................................................................................................................... 51 EVALUATION/TEST AND ANALYSIS ........................................................................................................... 51 CONSULTATION WITH SPECIALIZATION PERSONS................................................................................... 52

REQUIRMENTS SPECIFICATION .................................................................................................53




DOCUMENT CONTROL .................................................................................................................................. 53 REVISION HISTORY ....................................................................................................................................... 53 DISTRIBUTION LIST ...................................................................................................................................... 53 RELATED DOCUMENTS ................................................................................................................................. 53 1 INTRODUCTION .......................................................................................................................................... 54

1.1 PURPOSE .................................................................................................................................................. 54 1.2 PROJECT SCOPE ...................................................................................................................................... 54 1.2.1 In Scope .................................................................................................................................................. 54 1.2.2 Out of Scope ......................................................................................................................................... 55

1.3 DOCUMENT SCOPE ................................................................................................................................. 55 1.4 DEFINITIONS, ACRONYMS, AND ABBREVIATIONS ............................................................................. 55

2 USER REQUIREMENTS DEFINITION ......................................................................................55 2.1 USER CHARACTERISTICS ....................................................................................................................... 55

3 REQUIREMENTS SPECIFICATION ...........................................................................................56 3.1 FUNCTIONAL REQUIREMENTS ............................................................................................................. 56 3.1.1 USE CASE DIAGRAM – OVERALL FUNCTIONAL REQUIREMENTS ................... .................... ......... 57

3.1.2 REQUIREMENT 1: ACQUIRE DATA 1 AND 2 ................................................................................... 57 3.1.2.1 Description & Priority ................................................................................................................. 57 3.1.2.2 Use Case.............................................................................................................................................. 58 Scope .................................................................................................................................................................. 58 Description ...................................................................................................................................................... 58 Use Case Diagram ........................................................................................................................................ 58 Flow Description ........................................................................................................................................... 58

3.1.3 REQUIREMENT 2: CLEAN DATA 1 AND 2 ....................................................................................... 60 3.1.3.1 Description & Priority ................................................................................................................. 60 3.1.3.2 Use Case.............................................................................................................................................. 60 Scope .................................................................................................................................................................. 60 Description ...................................................................................................................................................... 60 Use Case Diagram ........................................................................................................................................ 61 Flow Description ........................................................................................................................................... 61

3.1.4 REQUIREMENT 2: ANALYZE DATA .................................................................................................. 63 3.1.4.1 Description & Priority ................................................................................................................. 63 3.1.4.2 Use Case.............................................................................................................................................. 63 Scope .................................................................................................................................................................. 63 Description ...................................................................................................................................................... 63 Use Case Diagram ........................................................................................................................................ 64 Flow Description ........................................................................................................................................... 64

3.1.5 REQUIREMENT 2: PUBLISH DATA ................................................................................................... 65 3.1.5.1 Description & Priority ................................................................................................................. 65

3.1.5.2 Use Case.............................................................................................................................................. 66 Scope .................................................................................................................................................................. 66 Description ...................................................................................................................................................... 66 Use Case Diagram ........................................................................................................................................ 66 Flow Description ........................................................................................................................................... 67

3.2 NON-FUNCTIONAL REQUIREMENTS ................................................................................................... 68 3.2.1 Availability: Must Have .................................................................................................................. 68 3.2.2 Storage Requirements: Must Have ............................................................................................ 68 3.2.3 Connection Reliability: Must Have ............................................................................................ 68 3.2.4 Connection Speed: Must Have ..................................................................................................... 68 3.2.5 Backup and Recovery: Must Have ............................................................................................. 68 3.2.6 Program to clean data: Must Have ........................................................................................... 68 3.2.7 Software Analysis tools: Must Have .......................................................................................... 68 3.2.8 Communication Requirements: Must Have ........................................................................... 69




3.2.9 Security: Must Have ......................................................................................................................... 69 3.2.9 Data Validation: Must Have ......................................................................................................... 69

5 INTERFACE REQUIREMENTS ...................................................................................................69 5.1 GUI ........................................................................................................................................................... 69

An example of a analysis of tweets. ...................................................................................................... 69

Examples of tweets analyzed on Microsoft Excel and Geo Flow ............................................. 69 Analysis of tweets using R language .................................................................................................... 71 Example of Excel Data for intro to Regression. .............................................................................. 71 Example of analysis completed on R Studio. .................................................................................... 72

6 ANALYSIS EVOLUTION ...............................................................................................................72

PROGRESS MANAGEMENT REPORT 1 ......................................................................................73 DOCUMENT LOCATION ................................................................................................................................. 73 REVISION HISTORY ....................................................................................................................................... 73 APPROVALS .................................................................................................................................................... 73 DISTRIBUTION ............................................................................................................................................... 73 PURPOSE OF DOCUMENT ............................................................................................................................. 74 DATE OF REPORT ........................................................................................................................................... 74 PERIOD COVERED .......................................................................................................................................... 74 SCHEDULE STATUS ........................................................................................................................................ 74

Updated Gantt chart ................................................................................................................................... 74

DEFINITIONS, ACRONYMS, AND ABBREVIATIONS ..............................................................74

PRODUCTS COMPLETED DURING THIS PERIOD ..................................................................75

PROBLEMS.........................................................................................................................................75 ACTUAL ........................................................................................................................................................... 75 POTENTIAL ..................................................................................................................................................... 75 RAID LOG: ....................................................................................................................................................... 76

Risks .................................................................................................................................................................... 76 Assumptions .................................................................................................................................................... 77 Issues .................................................................................................................................................................. 77 Dependency ..................................................................................................................................................... 77

PRODUCTS DUE FOR COMPLETION ..........................................................................................77 PROJECT ISSUES STATUES ............................................................................................................................ 78

CONCLUSION .....................................................................................................................................78

PROGRESS MANAGEMENT REPORT 2 ......................................................................................79 DOCUMENT LOCATION ................................................................................................................................. 79 REVISION HISTORY ....................................................................................................................................... 79

APPROVALS .................................................................................................................................................... 79 DISTRIBUTION ............................................................................................................................................... 79 PURPOSE OF DOCUMENT ............................................................................................................................. 80 DATE OF REPORT ........................................................................................................................................... 80 PERIOD COVERED .......................................................................................................................................... 80 SCHEDULE STATUS ........................................................................................................................................ 80




PROBLEMS.........................................................................................................................................81

ACTUAL ........................................................................................................................................................... 81 POTENTIAL ..................................................................................................................................................... 81




RAID LOG: ....................................................................................................................................................... 82 Risks .................................................................................................................................................................... 82 Assumptions .................................................................................................................................................... 83 Issues .................................................................................................................................................................. 83 Dependency ..................................................................................................................................................... 84

PRODUCTS DUE FOR COMPLETION ..........................................................................................84 CONCLUSION .....................................................................................................................................85

PROGRESS MANAGEMENT REPORT 3 ......................................................................................85 DOCUMENT LOCATION ................................................................................................................................. 85 REVISION HISTORY ....................................................................................................................................... 85 APPROVALS .................................................................................................................................................... 85 DISTRIBUTION ............................................................................................................................................... 85 PURPOSE OF DOCUMENT ............................................................................................................................. 86 DATE OF REPORT ........................................................................................................................................... 86 PERIOD COVERED .......................................................................................................................................... 86 SCHEDULE STATUS ........................................................................................................................................ 86




PROBLEMS.........................................................................................................................................87 ACTUAL ........................................................................................................................................................... 87 POTENTIAL ..................................................................................................................................................... 87 RAID LOG: ....................................................................................................................................................... 87

Risks .................................................................................................................................................................... 87 Assumptions .................................................................................................................................................... 88 Issues .................................................................................................................................................................. 88 Dependency ..................................................................................................................................................... 89

PRODUCTS DUE FOR COMPLETION ..........................................................................................89

CONCLUSION .....................................................................................................................................89

REFERENCES .....................................................................................................................................90




AbstractThis thesis investigates the possibility of predicting stock market movementusing Twitter activity. The Analysis will use data mining applications, dataanalysis techniques, correlation and regression modelling.The data mining of Twitter feeds was carried out.The process involved using Twitter API and Java code to search and downloadtweets with the words Apple, Microsoft and Tesla in them. These files were thenprocessed using Amazon web service and Text Wrangler. An analysis was carriedout using software such as R studio and Microsoft excel. Correlation models andRegression models were built along with the Granger Causality test in R studio.Visualisation techniques were carried out in Microsoft Excel and R studioshowing some trends in the data.A formula for stock market prediction for commercial use was created. Since thedata set gathered from Twitter was not large enough and the actual informationin the tweets was not specified towards the stock belonging to the companies,

there is an issue of noisy data corrupting the analysis. A sentiment analysis wasnot carried out on the tweets.

Definitions, Acronyms, and Abbreviations

Term Definition

API Application programming interfaceAWS Amazon Web Service

Causative A form that indicates that a subject causes something elseto do something or causes a change in state of a non-volition event.

GPOMS Google Profile of Mood States, algorithm to classify publicsentiment into 6 categories {Calm, Alert, Sure, Vital, Kindand Happy}

Granger causalitytest

A statistical hypothesis test for predicting if one time seriesis useful in predicting another.

NASDAQ National Association of Securities Dealers AutomatedQuotations

Noisy Data Meaningless data.

POMS Profile of Mood States.

Sentiment analysis A natural language processing, text analysis andcomputational linguistics to identify and extract subjectiveinformation in source materials.

Text Wrangler Text editor for Mac OS X

Tweet A message posted on the Twitter website.




Introduction

The stock market is an essential way for companies to raise money.

Companies can raise additional financial capital by being publicly traded in orderto expand their business by selling shares of ownership.Historically it is known that share prices can have a major influence on economicactivities and can be an indicator of social mood.The stock market movements has always been a rich and interesting subject withsuch many factors to be analysed that for a long time it would be consideredunpredictable.The application of new computerized mathematical methods over the past fewdecades developed by companies such as Merrill Lynch and other financialmanagement companies have created models that can maximize their returns

while minimizing their risks.

Stock market prediction has been around for years but it has been giving a newmethod of prediction thanks to the rise of social media.The objective of this project is to analyse Twitter feeds for activities and trendsassociated with a brand and to see how their stock market shares are related andif they are affected to the twitter activity.

This analysis will look at the relationship of the amount of tweets for threespecific brands on the NASDAQ, Apple, Microsoft and Tesla. The search for eachcompany’s symbols on the NASDAQ within those returned tweets would be

conducted as an additional exploration of stock conversation on Twitter.These brands where chosen since they are innovative technology companies thatare on the same stock exchange. Therefore gathering of the twitter data was nottime zone dependent.

Stock market data was collected from the Yahoo Finance website, there theyprovide historical data for the NASDAQ.Java scripts were used to acquire the tweets through Twitters API service.The Tweets for each brand were then counted using Amazon Web Service andText Wrangler.

The counted tweets were subsequently analysed using R studio werecorrelational and regression models were built and Granger Causality Test wasperformed.The Data was then visualised in Excel and R studio and the creation of a formulafor commercial use was attempted.




Related Work

In the previous study Stock Market Prediction Using Twitter I researched papersin relation to sentiment analysis of social media for the prediction of stockmarket movement. The social media in question was Twitter.The investigated looked at the correlation between the public mood and thestock market movement and how it can be used to predict stock market prices.The use of sentiment analysis was used to translate the tweets into moods usingalgorithms such as Google Profile of Mood States.The process of using a sentiment analysis on the tweets proved to be an accurateanalysis of the data.Analysing Twitter activity does not provide sufficient behavioural attitudestowards the investors and an accurate prediction of stock movement cannot beascertained. Sentiment analysis provides the investigation with an insight intothe public attitude. The more detailed sentiment analysis on the Twitter data

along with a reliable stock data the more superior and accurate the results.Twitter activity along might not give the insight the stockbroker needs to makechallenging decisions in buying or selling shares.

Systems and Datasets

Design and Architecture

Brief description of work carried out

The system was designed to acquire twitter and stock market data and comparethe two data sets for a relationship.

For the Twitter data the use of JAVA script, AWS script and Text Wranglerwere used to clean the data.

The financial data was acquired from the Yahoo Finance website. The datawas downloaded in excel format then saved as a CSV file.

Then the results from the cleaned Twitter data were placed with thefinancial cleaned data in excel.

Grangers Causality implemented in R Studio to find if the Twitter time’s series was useful at forecasting the stock prices time series.

A correlation model was built to confirm the relation between the twodata types.

Then excel was used to visualizes and confirm the relation.

Datasets

There were two forms of datasets.The first dataset acquired was the Twitter feeds.Historical tweets proved to be difficult since Twitter had sold on theirinformation to external parties. These companies, such as DataSift offer analysison historical data. While this would have been beneficial to the original projectproposal the budget of the project was zero.

Twitter launched a Historical Data Grant scheme, which allowed academicstudents to send in their proposal to gain access to Twitters historical data.




A proposal on behalf of this project was sent into the Data Grant scheme but areply from Twitter returned far too late into the project.

Subsequently from these dates the historical stock market data was gatheredfrom Yahoo Finance.

Gathering of Twitter Data.

The Java script was acquired under approval of Dr. Brian Mac Namee, a PrincipalInvestigator with CeADAR and a lecturer in the School of Computing at theDublin Institute of Technology. The Java script was used in conjunction with Twitter API.In order to use the Twitter API user must first sign up for a developer accountand create an application; there the user can acquire the API codes/keys to runtheir script.

The script was run on my behalf at a friend’s home since my own personal Internet connection was not suitable and the apprehension of disconnection,which would have returned unreliable time series.

Figure 1.1: Example of the application used in twitter. (Dev.twitter.com, 2014)




Figure 1.2: Example of the JAVA code used for downloading the twitter feeds.

Figure 1.3: Demonstrates where the unique keys were inputted into the JAVAscript.

Figure 1.4: Demonstrates where the key words were inputted into the JAVA

script.




Java script Issues

Since the returns from the JAVA script were so regular and to avoid any

apprehension of a system crash the data was saved into text files daily.The data sets retrieved from twitter were from 60 megabytes to 100 megabyteswith over 400,000 lines of tweets per day.Five sets of text files were attained representing Monday to Friday the NASDAQopening times.

Figure 1.5: Example of the acquired twitter feeds from the JAVA script in a textfile.

Since one of the days the script was running stopped there was a gap of whichexisted no tweets from 3am until 8am one day because of this tweets that werepublished between the trading times of the NASDAQ were used.NASDAQ trading hours is from 09:30 until 16:00 Monday to Friday.In GMT time that is 14:30 to 21:00.

Counting the Tweets

Next the tweets had to be counted.To this I initially proposed using Amazon Web Services because of the size of thedata sets. A word count from the AWS website was used to count all the specificwords in each tweet.




Figure 1.6: Example of the acquired Python script file from the AWS website.(Aws.amazon.com, 2014)

A folder in the S3 bucket was created named project 2014.Here all necessary files such as python scripts and tweet files were uploaded.An Elastic Map Reduce Cluster was created.

Figure 1.7: Example of a successful cluster from the AWS website.

(Aws.amazon.com, 2014)




Figure 1.8: Example of a text file returned form the AWS.

Word counting Issues

The drawback to this script file is that it counted each time a specific word cameup in a tweet providing results that were inaccurate.




Figure 1.9: Example of a tweet with Apple mentioned twice in Text Wrangler.(Mac App Store, 2014)

What was needed was a way to count the amount of tweets that had the keywordmentioned in them. These tweets could contain all three keywords (Apple,

Microsoft and Tesla) or together the twitter feeds of each word separately.

Text Wrangler was used to search the individual text files for the frequency ofthe tweets with the key words separately but still had the same problem ofcounting the amount of times the word occurred.

Figure 1.10: Example of tweets from Monday with Tesla mentioned, 3866occurrences in Text Wrangler.

(Mac App Store, 2014)

For this reason there will be some conflicts in my analysis result because of extraword counts in tweets with the keywords mentioned twice.

Date Apple AAPL Microsoft MSFT Tesla TSLA

07/04/2014 71913 1001 36417 521 3866 281

08/04/2014 118077 950 47925 613 4600 395

09/04/2014 81840 1100 24084 437 3113 301

10/04/2014 63983 1483 19521 435 3204 447

11/04/2014 62755 1145 18146 343 2140 347

Figure 1.11: Displays the key words and their occurrences per day.

The Original Key words were Apple, Microsoft and Tesla. I decide to also searchfor their NASDAQ symbol/code. From previous research into twitter mining and

stock prediction researchers searched for the company codes, as it would return




more accurate tweet count where people were tweeting about the actual stock ofthe company.

Gathering of Stock Price Data

Once the twitter feeds had being gathered the financial data could bedownloaded. The historical stock prices had to be the same dates as the Twitterfeeds. The data was downloaded in excel format then saved as a CSV file for usein R for analysis.Historical data sets of stock prices can only obtained per day at the minimumfrom Yahoo Finance otherwise it would have to be streamed from directly fromthe NASDAQ website, which I did not have the access to.Ideally hourly stock prices would have worked by matching the time series withthe Twitter feeds.Data sets of stock prices were collected from the Yahoo Finance website for allthree companies.

Each set had seven columns consisting of Date, Open, High, Low, Close, Volumeand Adjusted Close.

Date is the day of trading.

Open is the opening price of the stock at the start of the days trading.

High is the highest price of the stock form that day.

Low is the lowest price of the stock from that day.

Close is the closing price of the stock at the end of the days trading.

Volume the number of shares traded that day.

Adjusted Close is the after trading hours price. The difference betweenthe open and close price.




Figure 1.6: Demonstrates the acquired historical Apple stock prices for themonth of April 2014 form the Yahoo Finance website. (Finance.yahoo.com, 2014)

The closing price is the data in which this analysis focoused on.

Data Preparation

Results from the cleaned Twitter data were placed with the financial cleaneddata in excel.

Date Open High Low Close Volume AdjClose

Apple AAPL

2014-

04-11

519 522.83 517.14 519.61 9704200 516.72 62755 1145

2014-04-10

530.68 532.24 523.17 523.48 8559000 520.57 63983 1483

2014-04-09

522.64 530.49 522.02 530.32 7363200 527.37 81840 1100

2014-04-08

525.19 526.12 518.7 523.44 8710300 520.53 118077 950

2014-04-07

528.02 530.9 521.89 523.47 10351800 520.56 71913 1001

Figure 4.2: Displays the key words and their occurrences per day with the stock

prices for Apple.This was repeated for all three companies.




Requirements

The requirements have remained mostly the same from the originalRequirements Specification except for the use of live data rather than usinghistorical Twitter data. Historical Twitter proved to be impracticable as theproject had no budget and the historical data had to be purchased.

Data requirements

DR# Category Description Mo

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a

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DR1 Use ofInfromation

The information produced must be of use to the user. S M

DR2 Availability Information generated must not be previously available to

the user.

S L

DR3 Access The user must have access to this information. M H

User requirements

UR# Category Description Mo

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a

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UR1 Analysisoutcome

The analysis will provide Apple, Microsoft and Tesla with abetter insight of the effectiveness of their advertisingcampaign strategy form data acquired by the Twitter feedsand stock market.

S M

UR2 User outcome This information must be of assistance to these companies M M

Usability requirements

Functional Requirements

FR# Category Description Mo

sco

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FR1 Aquire Data 1 The project will gather and store all nessary data from liveTwitter feeds using JAVA scripts in conjunction with Twitter M H




API.

FR2 Aquire Data 2 The project will gather and store all nessary historical stockmrket data regarding the brand corrosponding to the datesin relation to the Twitter data that was aquired from theYahoo Finance website.

M H

FR3 Clean Data 2 The correct programs will be aquired and used to clean andretrive Twitter data regarding to key words and hash tags ofthe brand on certain dates.

M H

FR4 Clean Data 2 The correct programs will be aquired and used to clean andretrive data historcal stock market share prices regardingthe brand on the same time series as the Twitter feeds data.

M H

FR5 Analyse 1 The cleaned Twitter data is then analysed and compared. M H

FR6 Analyse 2 The cleaned stock market data is then analysed andcompared.

M H

FR7 Publish Data The analyse will then be publised and avslible to thecoustomer.

M H




Testing and Evaluation

Systems Testing.

Correlation

Correlation coefficient is the linear relationship between two variables. Alsoknow as Pearson Product-Moment Correlation Coefficient.Correlation values can be on a scale of +1 to -1.+1 for very story positive relationship.-1 for a strong negative relationship.

Regression

Regression is used to estimate or predict the relationships among onequantitative variable with another quantitative variable.

Granger CausalityGranger Causality is a statistical hypothesis test for predicting if one time seriesis useful in predicting another.Steps in testing stage

1. Check for correlation in R studio.2. Compose a regression model.3. Use Granger Causality test used to test if one time series is useful at

forecasting another.4. Change time series to adjust for lag.5. Excel and R studio to visualizes and confirm any relation.

Data sets.

The data sets used are the counts from the keyword searches from the AWSreturns. Apple, Microsoft and Tesla.Also the counts of the NASDAQ symbols for each company within those initialcounts will be used as an additional investigation AAPL, MSFT and TSLA.

Apple Stock

1. Check for correlation

Figure 4.3: Displays the file AprilAAPL imported into R studio.

First the data is imported into R studio.




Figure 4.4: Displays the correlation output in R.

The correlation model result shows a moderate relation between Close and thecounts of the keyword Apple of 0.223.

2. Regression Model

Figure 4.5: Displays the regression model output in R.

lm(formula = Apple ~ Close, data = AprilAAPL)

Does Apple tweet count have an effect the close price?

From the Multiple R-squared it is possible to see that the regression modelreturned a poor result with only 4.8% explaining Close price.

The process was carried out for the AAPL count.




Figure 4.6 Displays the regression model output in R.

lm(formula = AAPL ~ Close, data = AprilAAPL)

Does Apple tweet count have an effect the close price?

The regression model returned a similar poor result with only 0.07% explainingClose price.

3. Granger Causality Test

Close is Dependent and Apple is independent.Is Apple the cause of the effect of Close?Does Apple Granger cause Close?

Figure 4.7 Displays Granger Causality Test output in R for Closing price and

Apple word count.

From the result above you can see that after one-day lag are P value is 0.7057.




This is more than the significance level of 5%. Therefore the rejection of the Nullhypothesis cannot happen meaning Apple word count does not predict theclosing price one day later.

Figure 4.8 Displays Granger Causality Test output in R Closing price and AAPLword count.

A similar test was performed use the keyword AAPL as the independent andClose as the dependent. Results were slight better but did not cause GrangerCausality. P value of 24% >5%.

Since the data set was small a lag of 2 days could not be performed.

Figure 4.9 Displays Granger Causality Test unsuccessful outputs.

The above image demonstrates the unsuccessful outputs of the Granger Causalitytest using more than 1 day’s lag. The reason for this error is because the data setwas too small.




4. Visualization.

Figure 4.1.1 demonstrates the relationship between the Apple count and Close price.

From the above graph it is possible to see the positive relationship that thekeyword Apple has with the Close price of Apple stock. As the Apple Count rises

there is a rise in the closing stock price.

Figure 4.1.2 demonstrates the relationship between the AAPL count and Close price.




From the above graph it is possible to see the negative relationship that thekeyword AAPL has with the Close price of Apple stock. As the AAPL Count risesthere is a decline in the closing stock price. This proves are negative results fromthe correlation and regression models. AAPL was not a key word in the JAVA

script but a search within the key word apple.

Figure 4.1.3 demonstrates the relationship between the Apple count and Close price.

As you can see from the above chart the Close Price marked line follows a similartrend about a day later to the Apple count line.

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Apple count and Close Price

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Figure 4.1.4 demonstrates the relationship between the AAPL count and Close price.

Unfortunately the above chart shows that the Close price didn’t show a similartrend with AAPL but it actually showed a trend where AAPL word count isfollowing the Close Price.This is probably the reason the correlation model was so low between the two;also the investor community that would use the keyword AAPL (Apple stocksymbol) are disusing the rise in Apple stock.

Microsoft Stock

The process was started again this time using the Microsoft data set.

1. Check for correlation

Figure 4.1.5 demonstrates the correlation between Microsoft and MSFT word count andClose price.

The correlation model this time is much better with both keywords retuning a

moderate correlation with Close price.

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Close AAPL




2. Regression Model

Figure 4.1.6 displays the regression model with Microsoft word count as theindependent variable.

Figure 4.1.7 displays the regression model with MSFT word count as the independentvariable.

Figure 4.1.6 and 4.1.7 demonstration the two regression outputs from R as Closestock price as the dependent variable.Figure 4.1.6 displays a Multiple R-squared value of 0.96% explaining Close price.

Figure 4.1.7 displays a Multiple R-squared value of 12.6% explaining Close price.




The normality plot

If the residuals fall in a straight line that means the normality condition is met.

Figure 4.1.8 demonstrates Normality plot of Microsoft and Close price. Normality

condition is met.

Figure 4.1.9 demonstrates Normality plot of MSFT and Close price. Normality conditionis met.




3. Granger Causality Test

Figure 4.2.1 displays the Granger Causality.

Again the Granger Causality would not use a lag bigger tan one day. Bothreturned values bigger than the significant level of 5%.

4. Visualization

Figure 4.2.2 demonstrates the relationship between the Microsoft count and Close price.




Figure 4.2.3 demonstrates the relationship between the MSFT count and Close price.

Figure 4.2.4 demonstrates the relationship between the Microsoft count and Close priceon a line chart.

As you can see from the above chart the Close Price marked line follows a similartrend about a day later to the Microsoft count line.

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Microsoft and Close Price

Close Microsoft




Figure 4.2.5 demonstrates the relationship between the MSFT count and Close price on aline chart.

Pervious results with one day lag.

Figure 4.2.6 demonstrates the relationship between the Microsoft count and Close priceon a line chart with a one-day lag.

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C l o s e p r i c e

Microsoft and Close Price with 1 day lag

Close Microsoft




Figure 4.2.7 demonstrates the relationship between the MSFT count and Close price on aline chart with a one-day lag.

The decision was made to perform a manual lag in excel by moving the dates ofthe Microsoft count forward to see if the lines in the chart match up.This lag would mean that the tweet counts about Microsoft happened on thesame dates as the actual Closing price.The results from the two graphs show that visually there is a relationshipbetween the word counts and the Close stock price.

A correlation and regression model was built again using the lagged data.

1. Correlation

Figure 4.2.8 demonstrates the correlation between Microsoft and MSFT word count andClose price with a lag of one day.

The correlation model in figure 4.2.8 shown a strong correlation with the twoword counts. So a regression model was produced.

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MSFT andClose Price with 1 day lag

Close MSFT




2. Regression Model

Figure 4.2.9 displays the regression model with Microsoft word count as theindependent variable using data with a one-day lag.




Figure 4.3.1 displays the regression model with MSFT word count as the independentvariable with data of one-day lag.

The two regression models returned a high Multiple R-squared value of98%Figure explaining Close price.

The high correlation and regression proved that there is a relation between thetweet counts and the closing stock price. The results were very high the reasonfor this occurrence would be the very small data set that was used.

Tesla Stock

The process was started again this time using the Tesla data set.Correlation and regression was performed with similar results from the perviousdata sets.

Figure 4.3.2 demonstrates the correlation between Microsoft and MSFT word count andClose price.

Figure 4.3.2 demonstrates the correlation between Microsoft and MSFT word count andClose price with a one-day lag.

The keyword Tesla showed a strong correlation with the Tesla closing stockprice from the lagged data set. TSLA still displayed a moderate correlation.




Figure 4.3.3 displays the regression model with Tesla word count as the independentvariable using data with a one-day lag.

Again the regression with the lagged data set showed a huge improvement thenthe non-lagged Tesla data.

Figure 4.3.4 demonstrates the relationship between the Tesla word count and Closeprice on a line chart.

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Tesla Count and Close Price

Close Tesla




Figure 4.3.5 demonstrates the relationship between the Tesla word count and Closeprice on a line chart with a one-day lag.

Figures 4.3.4 and 4.3.5 demonstrate the difference between the non-lagged andthe lagged data sets. Figure 4.3.5 demonstrates that the one-day in lag does makea difference to the results. It demonstrates a close relationship the Tesla counthas with the Close price.

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Tesla Count and Close Price with one day lag

Close Tesla




Formula For Predicting Stock Movement

The creation of a formula for commercial use was conducted. The small data sethad an impact on this work since the use of a lag between two the three days wasdesired. From pervious research Stock Market Prediction using Twitter it wasdiscovered that the tweets would predict stock movement two to three days

after the message was tweeted.Knowing the tweet volumes of a company for two consecutive days thepercentage of movement of tweets between those two days should in turn allowus to predict the movement in the company share price within in a two or threeday lag.

Formula Used

The percentage difference between two numbers

(| V1 - V2 | / ((V1 + V2)/2)) * 100

V1 = total company tweets on day one.

V2 = total company tweets on day two.

The formula was used to find the percentage difference between the stockmovement and the tweet movement.

Apple Stock Prediction

To save time the focus is only on the key word count of Microsoft.Calculate the percentage difference of Apple Tweets And Closing Price

Difference in

Apple Stock % Difference in Tweet Activity %

-5.73099E-05 0.019568162

Day one 0.005% Day One 1.96%

0.013143818 0.279089758Day Two 1.31% Day Two 27.91%

-0.012897873 0.442778592

Day Three 1.29% Day Three 44.28%

-0.007392833 -0.390965218

Day Four 0.73% Day Four 39.09%

Figure 4.3.6 demonstrates difference in Stock Close price and Tweet activity betweendays.




If the movement were not identical in percentage increase/ decrease then theformula would need to be adjusted. The movement in Tweet Activity was notproportionate (pro rata movement).

Figure 4.3.7 demonstrates the formula for predicting the third day using Close stockvalues.

Example of the formula process

Subtract the tweets of Day 1 from Day 2.The tweet volume has an increase of 1228 tweets, which represent1.9568% increase.

The Apple closing stock of Day 1 is $523.47.

Multiply it by 1.9568%This projects an increase of $10.29

Add this to the to the Day 1 share price(523.47 + 10.29) = $533.7

Closing price of Day 3 = $530.32

Formula projects a closing price of $533.76 against an actual closing priceof $530.32.

The difference in the projected actual price is $3.38

This represents a variance of 0.639%




The formula used here is a straight line (1:1 ratio)The Apple share prices increase at the same rate as the Twitter feeds within anerror level of just 0.639%.

Figure 4.3.8 demonstrates the formula for predicting the forth day using Close stockvalues.

The process was repeated this time using values to predict the fourth day.Unfortunately an error of 27.904% was returned.

Figure 4.3.9 demonstrates the formula for predicting the fifth day using Close stockvalues.

The process was repeated this time using values to predict the fifth day.Unfortunately an error of 47.25% was returned. The formula didn’t apply to thedays after the third.




Calculate the percentage difference of Apple Tweets And Low Price

Figure 4.4.1demonstrates the formula for predicting the third forth and fifth day usingLow Stock values.

Also considered was the formula used with the Low stock price to see if therewas a relation.The best day the formula applied to was predicting the third day with an error of1.89%.




Calculate the percentage difference of Microsoft Tweets And Volume

The use of Volume in the formula was also measured.

Figure 4.4.2 demonstrates the formula for predicting the third day using the volumevalues.

However this too had a high error rate of 30.23%.

Microsoft Stock Prediction

Calculate the percentage difference of Microsoft Tweets And Closing Price

Difference in Stock Difference in Tweet Activity0.000502513 0.316006261

Day one 0.05% Day One 31.60%

0.016323456 -0.497464789

Day Two 1.63% Day Two 49.74%

-0.027427724 -0.189461883

Day Three 2.74% Day Three 18.94%

-0.003810976 -0.070436965

Day Four 0.38% Day Four 7.04%


Projecting closing stock price Day 3




Figure 4.4.4 demonstrates the formula for predicting the third forth and fifth day usingthe Close stock values.

The formula returned a high variance for all projected days.

This concludes that the formula does not apply to any of these days using CloseStock.




Calculate the percentage difference of Microsoft Tweets And Low Price

Also considered was the formula used with the Low stock price to see if therewas a relation.

Tweets day1 - day2 11508

Low stock of day 1 * difference of tweets day1 and day 2 12.5580888

Stock low price day 1 + low stock of day 1 * difference of tweets day1 and day 2 52.2980888

Low price of Day3 - projected low price day 3 -12.5580888

Difference between projected low day 3 and actual day 3 as a variance. 0.237448234

23.74%

Figure 4.4.7 demonstrates the formula for predicting the third day using the Low stockvalues.

Again the formula showed that it did not apply to the Low Stock price.

Calculate the percentage difference of Microsoft Tweets And Volume

Figure 4.4.7 demonstrates the formula for predicting the third day using the Volumevalues.

The Volume data was placed into the formula but the result shown above has ahigh error rate of 44.5%.




Tesla Stock Prediction

Calculate the percentage difference of Tesla Tweets And Closing Price

Difference in Stock

Difference in Tweet

Activity

0.002007934 0.189860321

Day one 0.200793379 Day One 18.98603207

0.027922269 -0.32326087

Day Two 2.792226911 Day Two 32.32608696

-0.02110152 0.029232252

Day Three 2.110151951 Day Three 2.923225185

0.026816564 0.332084894

Day Four 2.681656439 Day Four 33.20848939





Figure 4.4.9 demonstrates the formula for predicting the third forth and fifth day usingthe Close stock values.

The formula had high percentage errors except for the prediction for the fifth

day with an error of 2.33%.




Tweets day1 - day2

low stock of day 1 * difference of tweets day1 and day 2 38.69163476

Stock low price day 1 + low stock of day 1 * difference of tweets day1 and day

2 242.4816348

Low price of Day3 - projected low price day 3

-

48.07163476

Difference between projected low day 3 and actual day 3 as a variance.

-

0.198248559

-

19.82485594Figure 4.5.1 demonstrates the formula for predicting the day using the Low stock values.

Tweets day1 - day2 -734

Volume day 1 * difference of tweets day1 and day 2 1369177.703

Volume day 1 + Volume day 1 * difference of tweets day1 and day 2 8580677.703

Volume Day3 - projected low price day 3

-

877677.7031

Difference between projected Volume day 3 and actual day 3 as a

variance.

-

0.102285359

-

10.22853594

Figure 4.4.9 demonstrates the formula for predicting the third day using the Volume

values.

When the Low Stock and Volume values were placed into the formula they alsodisplayed high errors. Low Stock had an error of over 19% and the Volumevalues had an error over 10%.




ConclusionThis analysis investigated the relation between twitter activity and stock marketshare prices of three companies in the NASDAQ over a period of one week. Theuse of a Java script and Twitters API collected the tweets that had the keywordsApple, Microsoft and Tesla mentioned in them. Once the tweets were collected apython file was used to count the frequency of words in conjunction withAmazon Web Service. AWS was used because of the size of the Tweets files,which were in text format of sizes ranging from 60 to 130 megabytes.Text Wrangler was also used to count the frequency of tweets with thekeywords. Since one of the data sets have missing data over five hours due to aprogram failure it was decided to use tweets during the NASDAQ trading hours.Stock data belonging to the three companies was acquired from the YahooFinance website.Similarly a count of times the NASDAQ symbols for each company was conductedand used as an additional analysis. The symbols would give the opportunity to

investigate the occurrence of conversations directed to the actual company stockon the NASDAQ.Analysis was performed in R studio using a correlation model first to see the howstrong a relation the tweet data had with the stock data of each company.A Linear regression algorithm was then used to see the effect that the twitterdata had on the stock data.Granger Causality was performed to discover if one of the time series affectedthe other providing a result in the form of a lag per day. Since the data was sosmall a lag of only one-day could be performed providing a significant level ofover 5%, which we could not select, the alternative hypothesis.During visualization of the data using line graphs it was noted that there seem tobe a relation where the stock data had a similar trend one day after the tweetdata. A manual lag was performed in excel by moving the tweet data time seriesforward by one day. This proved that a trend did exist. Subsequently acorrelation model in R studio was created and the results exhibit a strongcorrelation of 0.9 and over.The creation of a formula for commercial use was attempted. The first formulawas used to find the percentage difference between the stock movement and thetweet movement. On average there was a difference between the movement ofthe stocks and the shares.Another formula was created to predict the close share price. Knowing the

twitter volumes of a company for two consecutive days, the percentage ofmovement of tweets between those two days should in turn allow us to predictthe movement in the company share price three days later.The formula used is a straight line (1:1 ratio)Whilst predicting the third day for the Apple share prices an error level of just0.639% was returned.This meant that the close share price increased at the same rate as the Twitterfeeds for the key word Apple. Within an error lever of 0.639%Disappointingly the other days predicted for Apple Close stock price were not assuitable returning error rates of 27.9% and 47.25%. This trend continuedthroughout the analysis for the closing price in the Microsoft and Tesla stock.

The formula was slightly altered to accommodate the use of other variables suchas Low Close stock and Volume. Again the errors were high for each one.




The main issue here is that the data set is not developed enough to do this formof analysis. When acquiring the data specific tweets regarding the stock of thecompany should have only being collected. A company on Twitter is competingfor public interest while the stock exchange is competing for capital interest. Inthat aspect some of the Tweets gathered in this analysis are noisy data.

Further Development

Further develop in the project would include extracting tweets and stockdata over a longer period of time. This would have provided the analysiswith a superior result from the Granger Causality test.

The tweets need to be selected form a niche community, preferably the

investor community who communicate through Twitter in relation to thestocks of companies. Tweets that have the company symbols and theword “stock” mentioned in them should be gathered using thosekeywords.

Narrowing down the selection of companies and focusing on one wouldsupport in reducing the amount of discrepancies in the tweet count.

Developing a program script to count the lines that a word appears inwithout recounting the word again if it has being mentioned more thanonce in a tweet.

The potential use of developing a formula that could take account of othervariables that would cause movement in stock, such as events like the

release of company financial reports, takeover rumours, mergers or badpublicity.

The process of using a sentiment analysis on the tweets would provide amore accurate result from the data. Analysing Twitter data activity alongwill not provide the analysis with any information about behaviouralattitudes towards the investors.

Sentiment analysis would also provide a better insight into the publicattitude.




Bibliography

Aws.amazon.com, (2014). Word Count Example : Articles & Tutorials : Amazon

Web Services. [online] Available at: http://aws.amazon.com/articles/2273(Accessed 22 May. 2014).

Bollen, J. and Mao, H. (2011) 'Twitter mood as a stock market predictor'Computer .

Datasift.com, (2014). Power Decisions With Social Data | DataSift . [online]Available at: http://datasift.com (Accessed 24 May. 2014).

Dev.twitter.com, (2014). Twitter Developers. [online] Available at:https://dev.twitter.com (Accessed 22 May. 2014).

Finance.yahoo.com, (2014). AAPL Historical Prices | Apple Inc. Stock - Yahoo!

Finance. [online] Available at:http://finance.yahoo.com/q/hp?s=AAPL&a=03&b=01&c=2014&d=03&e=30&f=2014&g=d (Accessed 22 May. 2014).

Mac App Store, (2014). TextWrangler . [online] Available at:https://itunes.apple.com/ie/app/textwrangler/id404010395?mt=12 (Accessed22 May. 2014).

Mittal, A. and Goel, A. (2012) 'Stock prediction using Twitter sentiment analysis'Standford University, CS229(2011 http://cs229. stanford.

edu/proj2011/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis.

pdf).

Simsek, M. and Ozdemir, S. (2012) 'Analysis of the relation between Turkishtwitter messages and stock market index'.

Ucd.ie, (2014). CeADAR. [online] Available at: http://www.ucd.ie/ceadar/(Accessed 26 May. 2014).

Ucd.ie, (2014). Brian Mac Namee | CeADAR. [online] Available at:http://www.ucd.ie/ceadar/people/principalinvestigators/brianmacnamee/(Accessed 26 May. 2014).

Appendix

Project Materials:

https://drive.google.com/folderview?id=0B4pkBIaL1W7CQzVVakgwQ3psNFk&

usp=sharingReferences

https://drive.google.com/folderview?id=0B4pkBIaL1W7CQzVVakgwQ3psNFk&usp=sharingReferences








Project Proposal

Introduction

The purpose of this project is to study and analyse the activities and trendsassociated to the Mobile World Congress 2014, which is being held from the 24 th to the 27th of February 2014.The Mobile World Congress is the world’s largest exhibition of the mobileindustry. Mobile operators, device manufacturers and technology providers areall represented at the exhibition.With a large amount of manufacturers attending and product launches thesubject can be quite broad.

The objective of this project is to analyse Twitter feeds for activity’s and trendsassociated with the top mobile manufacturers before, during and after the eventand to see how their stock market shares are connected and affected by theTwitter feeds.

Background

As Twitter matures, top brands have realized just how relevant Twitter can be asa marketing and engagement platform.According to Useful Social Media 98% of the top brands are on Twitter and 92%of top brands tweet daily. There are 230 million active users on Twitter; thisprovides brands with a global presence. (USM) “ 92% of top brands Tweet atleast once daily as audiences grow. Study shows Twitter’s maturity as amarketing and engagement platform. 98% of all top brands are active on Twitter.The social network has matured into a valuable and necessary channel formarketing organizations.” (Usefulsocialmedia.com, 2014)i

Releases such as the Samsung Galaxy s5 will hopefully see a surge of Twitteractivity in relation to Samsung during the event. According to Trusted Reviews

the release of the Samsung Galaxy s5 will take place during the event. (TrustedReviews) “The Samsung Galaxy S5 release date looks set to be held in a matter ofdays as the Korean manufacturer issues invites to a February 24 launch event,kicking Samsung Galaxy S5 rumours into overdrive.”(Trusted Reviews, 2014)ii

Using the data from the Twitter feeds I can then analyse them against the stockmarket shares.According to Mac Rumours, Samsung has the biggest phone market share withApple in second place. (Mac Rumours) “Apple Continues to Lose SmartphoneShare, Gain Mobile Phone Share in 4Q 2013” (Mac Rumours, 2014)iii




Similar research has being done in relation to Twitter feeds influencing marketshares but this project will be focusing mainly on the Mobile World Congress inrelation to the markets shares of the top five mobile device manufacturers.

Technical Approach

This objective will be achieved by:

Creating the necessary python coding to use with the Twitter API forretrieving the data.

Gathering all data created on Twitter related to the mobile device brandsbefore, during and after the event.

Gather stock market share prices before, during and after the event of themobile device brands.

Clean all data gathered for analysis

Analysis of the data gathered of Twitter activity against the stock marketshare prices.

Return the results of the analysis.

Special Resources Required

Books to be used:

Python for data analysis Mckinney, W. (2013)

Twitter API: Up and Running: Learn How to Build Applications with theTwitter API Paperback by Kevin Makice. (2009)

Writing Your Dissertation by Swetnam, D. & Swetnam, R. (2000).

Software to be used:

Python

R studio

MYSQL

Microsoft Excel

Microsoft Project

Twitter API

System storage to be used:

Twitter API

At this stage of the project I am unaware of the amount of data that I willaccumulate from Twitter.

http://www.amazon.com/Kevin-Makice/e/B002GBEI8G/ref=ntt_athr_dp_pel_1

http://www.amazon.com/Kevin-Makice/e/B002GBEI8G/ref=ntt_athr_dp_pel_1




Project Plan

Technical Details

The coding I will use to retrieve the data will be python.R coding and Microsoft Excel will then be used to do the analysis of the data.

Systems/Datasets

The datasets used will be all collected by myself using the online Twitter APIwith the python coding to collect specific words, hash tags from the tweets over

the duration of the events operating time per day.

Evaluation/Test and Analysis

I am unable to state how I will test the data due to the fact that we have only hadone class of Data and web mining but I can list the types of analysis that we willbe learning.

Classification

Regression (value estimation)

Similarity matching Clustering




Co-occurrence grouping (frequent itemset mining)

Profiling (behaviour description)

Link Prediction

Data reduction

Causal modelling

Consultation with Specialization Persons

John O’Connor CEO of Wellclever.Wellclever is a startup company that provides the media groups and contentproducers with keyword contextual online advertising solutions.Consulted with John for project ideas. John has over 20 years of experience in theadvertising industry.

(Wellclever, 2014)iv

Oisin Creaner coordinator of the project for NCISpoke to Oisin about project ideas through the use of Twitter API’s.




Requirments Specification

Document Control

Revision History

Date Version Scope of Activity Prepared Reviewed Approved

20/02/2014 1 Create RC X X

23/02/2014 2 Update RC X X

24/02/2014 3 Update RC X X

Distribution List

Name Title Version

Oisin Creaner Lecturer

Samsung CustomerRobert Coyle BA

Robert Coyle System Developer

Robert Coyle Statistician

Robert Coyle Tester

Robert Coyle Advertising and Marketing Devision

Related Documents

Title Comments

Proposal Document




1 Introduction

1.1 Purpose

The purpose of this project is to study and analyze the activities and trendsassociated to a brands advertising campaign. The objective of this project is toanalyze Twitter feeds for activities and trends associated with the brand before,during and after their advertising campaign and to see how their stock marketshares are connected and affected by the Twitter feeds.The intended customers are the actual brands, their marketing and PR team.

As Twitter matures, top brands have realized just how relevant Twitter can be asa marketing and engagement platform.According to Useful Social Media 98% of the top brands are on Twitter and 92%of top brands tweet daily. There are 230 million active users on Twitter; thisprovides brands with a global presence. (USM) “ 92% of top brands Tweet atleast once daily as audiences grow. Study shows Twitter’s maturity as amarketing and engagement platform. 98% of all top brands are active on Twitter.The social network has matured into a valuable and necessary channel formarketing organizations.” (Usefulsocialmedia.com, 2014)v

1.2 Project Scope

This analysis will compare different advertising campaigns done by a brand onthe release of a new or updated product and how they differ from one another. Itwill also look at how a brands advertising campaign affects their stock marketshare prices.I will be using the historic Twitter feeds and historic stock market shares.The project will look at an individual brand such as Samsung, acquire thenecessary twitter feeds associated with Samsung. Using the correct programsand scripts the program should gather any mentions of Samsung in the tweetsincluding hash tags.The data will include the time series of the tweets and then we can match thisdata to the time series of the stock market data.

With a budget of zero acclimating the historic Twitter feeds could be a difficulttask since my researching has show that Twitter has giving/sold their data toseparate/outside companies who now sell the data for use.

1.2.1 In Scope

1. The analysis of a advertising campaign with the data gathered fromtwitter and stock market share prices.

2. The development of python programs for cleaning data.

3. The development of an R program and the use of Microsoft Excel for

the analysis of the data.




1.2.2 Out of Scope

1. The project will not provide Samsung with outside analysis of other

brands data.

1.3 Document ScopeThe goal of this document is to describe the functional and non-functionalrequirements of the Samsung advertising campaign analysis. The stakeholderanalysis was carried out prior to requirement elicitation process.

1.4 Definitions, Acronyms, and Abbreviations

Term Definition

Advertising

campaign

A series of messages to promote a product.

BA Business Analyst

Backed-up The process of storing information (hardware or software based)

Cloud Internet based service where storage, applications and servers areaccused through the internet for an organization.

Data Information

Excel Microsoft Excel is a spreadsheet application used here for analyzingdata.

GUI Graphical user interface

Moscow Is a technique used in functional requirements .Must, Could, Should,Want. See Functional requirements

Pyton Type of programming language

R Programming Langauge

2 User Requirements Definition

2.1 User Characteristics

As part of Samsung’s $14 billion advertising and marketing campaign last year(2013) the company requires an analysis on the effectiveness of the advertisingcampaign and how the twitter activity and their stock market prices wereaffected. According to ibtimes.co.uk Samsung were expected to spend $14 billionon there marketing campaign (ibtimes.co.uk) “The South Korean company isexpected to spend around $14 billion (£8.5bn, €10.3bn) on marketing andpromotion of its products in 2013, which is the biggest (as a percentage of itstotal revenue) advertising budget of any company – ever”(ibtimes 2013)vi,Samsung have not yet released there analog report for 2014.The analysis will provide Samsung with a better insight of the effectiveness oftheir advertising campaign strategy form data acquired by the Twitter feeds and

stock market. This information will assist Samsung in managing their advertising

http://www.reuters.com/article/2013/11/27/us-samsung-marketing-idUSBRE9AQ18720131127?type=companyNews

http://www.reuters.com/article/2013/11/27/us-samsung-marketing-idUSBRE9AQ18720131127?type=companyNews




campaign more effectively and efficiently by directing the style and approach ofthe campaign towards their specific products.

3 Requirements Specification

3.1 Functional Requirements

FR# Category Description Mo

sco

w

S

t

a

t

u

s

FR1 Aquire Data 1 The project will gather and store all nessary data from

historical Twitter feeds.

M H

FR2 Aquire Data 2 The project will gather and store all nessary historical stockmrket data regarding the brand corrosponding to the datesin relation to the Twitter data that was aquired.

M H

FR3 Clean Data 2 The correct programs will be aquired and used to clean andretrive histoical Twitter data regarding to key words andhash tags of the brand on certain dates.

M H

FR4 Clean Data 2 The correct programs will be aquired and used to clean andretrive data historcal stock market share prices regardingthe brand on the same time and dates as the histoical Twitterfeeds data.

M H

FR5 Analyse 1 The cleaned Twitter data is then analysed and compared. M HFR6 Analyse 2 The cleaned stock market data is then analysed and

compared.M H

FR7 Publish Data The analyse will then be publised and avslible to thecoustomer.

M H




3.1.1 Use Case Diagram –

Overall Functional Requirements

3.1.2 Requirement 1: Acquire Data 1 and 2

3.1.2.1 Description & Priority

The scope of this use case is to gather all the data necessary to carrier out the

analysis and continue onto the next stage of the project. This requirement has avery high status and is essential in progressing on the next stage of the analysis.




3.1.2.2 Use Case

Scope

The system shall source the historic twitter and stock market data from onlinedata resources. Define all access points. Accuses the Data, notify its availability

and then download the data.

Description

This use case describes the process to which the data for analysis is acquired.

Use Case Diagram

Flow Description

Precondition

The Data must be online. The data system must be operational at all times.




Activation

Use case is activated when the programmer connects to the system online.

Main Flow

1. Step: 1A. Programmer and System Developer source data.

2. Step: 2A. Programmer and Business Analyst validate data with theCustomer.

3. Step: 3A. Programmer accesses the data.

4. Step: 4A. Programmer notifies data availability to the System

Developer.

5. Step: 5A. Programmer downloads data for cleaning.

Alternate Flow

1. Step: 1A. Programmer and System Developer source data.2. Step: 2A. Programmer and Business Analyst validate data with the

Customer.

3. Step: 2A. Customer does not validate data. Step 1A is set to

recommence.


5. Step: 2A. Programmer and Business Analyst validate data with the

Customer.

6. Step: 3A. Programmer accesses the data.

7. Step: 4A. Programmer notifies data availability to the SystemDeveloper.

8. Step: 5A. Programmer downloads data for cleaning.

Exceptional Flow


2. Step: 2A. Programmer and Business Analyst validate data with the

Customer.

3. Step: 2A. Customer does not validate data. Data is unavailable.

4. Use case ends

Termination

The system has gathered all necessary data. The data is then exported on thecloud storage system. This process has now being terminated.

Post Condition

All Data gathered, move onto the next step.




3.1.3 Requirement 2: Clean Data 1 and 2


The scope of this use case is to clean all the data gathered from the perviousrequirement. A programmer and tester investigate the data for any errors such

as missing data and fix the errors. This requirement has a very high status and isessential in progressing on the next stage of the analysis.

3.1.3.2 Use Case

Scope

The system shall clean all data sets gathered from the pervious requirement.Define all error points. Get recommendations for fixing the errors. Fixes theerrors and then exports the data for analysis.

Description

This use case describes the process to which the data is cleaned for analysis.




Use Case Diagram

Flow Description

PreconditionThe Data must be stored and available for cleaning at all times.

Activation

Use case is activated when the programmer connects to the cloud storage systemand retrieves the data.

Main Flow

1. Step: 1B. Programmer and System Developer retrieve data from the

cloud storage system.

2. Step: 2B. Programmer and Tester identify errors in the data set.

3. Step: 3B. Programmer receives recommendations from SystemDeveloper.




4. Step: 4B. Programmer with the help of the Tester fixes errors and

notifies the System Developer.

5. Step: 5B. Programmer exports the data for analysis.

Alternate Flow




3. Step: 3B. Programmer receives recommendations from System

Developer.



5. Step: 2B. Programmer and Tester test system again and identify more

errors in the data set.


Developer.



8. Step: 5B. Programmer exports the data for analysis.

Exceptional Flow





Developer.

4. Step: 4B. Programmer with the help of the Tester fixes cannot fix

errors. Data is corrupt.

5. Use case ends.

Termination

The system cleaned all acquired data. The data is then saved onto the cloudstorage system and exported for analysis. This process has now beingterminated.

Post Condition

All data cleaned, move onto the next step.




3.1.4 Requirement 2: Analyze Data


The scope of this use case is to analyze all the data gathered and cleaned fromthe pervious requirements. A Business Analyst and Statistician examine and

study the data for Analysis. This requirement has a very high status and isessential in progressing on the next stage of the analysis.

3.1.4.2 Use Case

Scope

This process involves the skills and management of the Statistician and BusinessAnalyst to compare and analyze all data.The process shall calculate and prove/predict outcomes form the data with thehelp of graphs for visualizing. Then all proven data is backed-up and stored.

DescriptionThis use case describes the process to which the data analyzed.




Use Case Diagram

Flow Description

PreconditionThe Data must be available for analysis at all times.

Activation

Use case is activated when the BA and the Statistician connects to the cloudstorage system and retrieves the data.

Main Flow

1. Step: 1C. BA and Statistician retrieve data from the cloud storage

system.




2. Step: 2C. The Statistician and BA explore and understand the data set.

3. Step: 3C. Statistician begins the calculations.

4. Step: 4C. Statistician and BA began to visualize the data.

5. Step: 5C. Programmer backs up and stores findings with the approval

of the BA.

Alternate Flow


system.


3. Step: 3C. Statistician begins the calculations.

4. Step: 4C. Statistician and BA began to visualize the data. Ba requests

the data to be recalculated with a different approach.

5. Step: 3C. Statistician begins the new calculations.

6. Step: 4C. Statistician and BA began to visualize the data.

7. Step: 5C. Programmer backs up and stores findings with the approval

of the BA.

Exceptional Flow


system.


Statistician and BA are unable to understand the data set. Ba requests

new data set.

3. Use case ends

Termination

The analysis is completed. The data is then saved onto the cloud storage systemand exported for Publishing. This process has now being terminated.

Post ConditionAll data analyzed, move onto the next step.

3.1.5 Requirement 2: Publish Data


The scope of this use case is to publish the findings from the analysis approvedby the pervious requirements. A Business Analyst consults the Customer ontopics such as the proprietor of the data, the goal from the publication, the targetaudience/data consumer (is the data confidential and for internal use only),

media to which it is published and the release date.This requirement has a very high status.




3.1.5.2 Use Case

Scope

This process involves the communication and business skills of the BA and howto handle the customer’s requirements and outcomes.

The process involves the Customer, BA and the Advertising/Publicationsdivision.The process shall publicize the findings to the desired audience with theapproval of the customer and recommendations of the BA.

Description

This use case describes the process to which the data is publicized.

Use Case Diagram




Flow Description

Precondition

The Data must be available for analysis at all times.Customer/Client must be available for analysis at all times.

Activation

Use case is activated when the findings are present to BA, Customer andAdvertising/Publication Division and all three are engaged in communication.

Main Flow

1. Step: 1D. BA, Customer and Advertising/Publication Division retrieve

analysis findings. Findings have acquired owner’s approval.

2. Step: 2D. BA and Customer discuss the objective of the findings

release.

3. Step: 3D. BA and Customer began to agree on the target audience/data

consumer.

4. Step: 4D. Customer decides the medium type/the style and method of

publicizing the data e.g. websites, newspaper, with the BA’s approval

and the assistance of the Advertising/Publication Division.

5. Step: 5D. BA notifies Advertising/Publication Division to publish the

data.

Alternate Flow


analysis findings. Findings have acquired owner’s approval.

2. Step: 2D. BA and Customer discuss the objective of the findings

release.

3. Step: 3D. BA and Customer began to agree on the target audience/data

consumer.


publicizing the data e.g. websites, newspaper, with the BA’s approvaland the assistance of the Advertising/Publication Division. Customer

decides to recommence Step: 3D. Again to change the publication

approach.

5. Step: 3D. BA and Customer began to agree on a new target

audience/data consumer


publicizing the data e.g. websites, newspaper, with the BA’s approval

and the assistance of the Advertising/Publication Division.

7. Step: 5D. BA notifies Advertising/Publication Division to publish thedata.




Exceptional Flow


analysis findings. Findings have not acquired owner’s approval.

Customer decides not to publicize the data findings due to the highimportance and confidentiality of the findings.

2. Use case ends

Termination

The publication of the data is completed. This process has now being terminated.

Post Condition

All data publicize, all steps completed.

3.2 Non-Functional Requirements

3.2.1 Availability: Must Have

The information must be available at all times for analysis.

3.2.2 Storage Requirements: Must Have

The data kept during and after the analysis should be stored in a secure facility.Cloud storage security protocols must be assessed. The must be enough capacity

in the cloud to hold the large amount of data.

3.2.3 Connection Reliability: Must Have

It must have a reliable connection at all times when retrieving, uploading andupdating the data. Connection lost could transpire into losing data.

3.2.4 Connection Speed: Must Have

It must have fast online connection. This is needed when retrieving, uploadingand updating the data. A large data set could take some time to upload.

3.2.5 Backup and Recovery: Must HaveThe data must be easily accessed, backed up and updated. It must have a systemrecovery in the case of a system failure.

3.2.6 Program to clean data: Must Have

The analysis must have the correct programs to clean and fix any errors in thedata.

3.2.7 Software Analysis tools: Must Have

The analysis must have the correct software analysis tools that all divisions ofthe analysis can exercise.




3.2.8 Communication Requirements: Must Have

The analysis must have constant communication between all divisions/ partiesin the decision making process.

3.2.9 Security: Must Have

The analysis must have high security measures. The analysis is operating withhighly confidential data. Only key divisions from the analysis must have accusesto the data.

3.2.9 Data Validation: Must Have

This process requires the use of external services in order to download the data.Once the data is gathered from the services (Twitter, Nasdaq) it should bevalidated.

5 Interface Requirements

5.1 GUI

An example of a analysis of tweets.

vii comprendia. 2014

Examples of tweets analyzed on Microsoft Excel and Geo Flow




viii powerpivotblog. 2013




Analysis of tweets using R language

ix evolutionanalytics. 2013

Example of Excel Data for intro to Regression.

This is using stock market data.

x skilledup. 2013




Example of analysis completed on R Studio.

xi datamachines. 2012

6 Analysis EvolutionThe analysis will evolve over time to produce a much more focused outcome,differencing itself by the analysis of a specific product in the Samsung productrange. This can occur by changing the mining of keys words in the twitter data,focusing on a product such as the Galaxy products in the Samsung range. Theseinclude the smartphone, Tablet and Watch.If the customer “Samsung” required an analysis to focus on the release of aspecific product such as the Galaxy S4 which was released April 2013 this can bedone by narrowing down the search key word, using hash tags and words such

as (#samsungS4, #SamsungGalaxyS4, #GalaxyS4 #S4) and narrowing down thetime lines to the release date of the phone.




Progress Management Report 1

Document Location

This document will be uploaded through Turnitin.

Revision History

Date of this revision: 9/03/14

Revisiondate

Previsionrevisiondate

Summary of changes Changesmarked

9/03/14 First Issue

ApprovalsThis project requires the following approvals.

Name Signature Title Date of issue Version

Robert Coyle ProjectManager

10/03/14 1

Distribution

Name Title Date of issue Version

Oisin Creaner Project Lecturer 10/03/14 1




Purpose of Document Is to provide Oisin Creaner the project lecturer with a summary of the status ofthe project.

Date of report

09/03/14

Period covered

10/02/14 – 9/03/14

Schedule Status

This project is still on schedule at this interval.

Updated Gantt chart


Term Definition

API Application programming interface

JSON JavaScript Object Notation

NASDAQ American Stock Exchange

RSS Rich Site Summary

7

4

7

5

1

51

25

3

8

3

20

03-Feb 23-Feb 15-Mar 04-Apr 24-Apr

Project Proposal

Create Python codes

Data retrival from Twitter API and…


Management Progress Report 1





Products completed during this period

Project proposal The project proposal was completed on time. See(Coyle, 2014)

Requirements

specification

Requirements specification was completed ontime with changes t project scope. See (Coyle,2014)

Problems

Actual

Accessing Twitter API Twitter API has being more difficult to accessthan first anticipated due to change ofregulations and updated version of twitter. TheAPI only supports JSON.

Acquiring free historical

data.

Historical feeds are proving to be difficult, astwitter has sold their data to approved sites forresale. As this project has no budget this hasbeing a high impact on the plan. Twitter hasreleased a grant application form online foraccessing their historical data.

Potential

The quality and quantity of

the twitter data.

Not having the JSON code yet I am not sure whatmy expected returned of data will be. Using a site

called Twillert, I acquired some data but the sitewon’t gather more that the first 100 RSS feeds,this rendering the service useless.

Gathering the data in the

required time.

Once I have a response from the Twitterdevelopers grant I can determine whether thehistorical data is possible to acquire and progressto the next stage of the project.




Raid Log:

Risks




Assumptions

Issues

Dependency

Products due for completionBy the next period the following should be accomplished.

Gathering of Twitter feeds. Should have gathered all twitter data eitherhistorical or real time in relation to Samsung.

Gathering of stock market

data.

Should have gathered all Nasdaq data in relationto Samsung in the same time series as the twitterdata.

Analysis of data. Once all data has being gathered analysis can

take place.Preliminary presentation. Should have Preliminary presentation completed.




Projects write up. Commenced first draft.Management Progress

Report 2.

This repot will be the end of this period.

Project Issues StatuesWe currently have 2 issues on the project issue log, these haven’t being resolvedand are currant outstanding. Both are waiting upon external client response.

ConclusionThis project, even with the set backs is still capable of finishing within theoriginal set target dates. Gathering all the data in the next week is paramount forthe success of the project. Any more delays will compromise the quality of theproject.

Currently I am waiting on a response from Twitter in relation with theirDevelopers grant scheme. If this is approved all the historic data from January2013 to March 2014 will be available and can be gathered using JSON codinglanguage, See Dependences Ref: D02.All necessary information has being submitted to the Twitter Developer Grantscheme such as dates, key words and hash tags.

Alternatives:

If this grant is not approved the project can revert back to streaming the

data live form Twitter using JSON language. If the grant approval takes to long the project can revert back to

streaming the data live form Twitter using JSON language.





Document Location


Revision History


Revisiondate



30/03/14 First Issue

ApprovalsThis project requires the following approvals.

Name Signature Title Date of issue Version

Robert Coyle ProjectManager

30/03/14 1

Distribution






Purpose of Document

Is to provide Oisin Creaner the project lecturer with a summary of the status ofthe project.

Date of report

30/03/14

Period covered

10/03/14 – 30/03/14

Schedule Status


Updated Gantt chart


Term Definition

API Application programming interface




74

75

15

114

37

7

03-Feb 23-Feb 15-Mar 04-Apr 24-Apr 14-May

Project Proposal

Create Python codes









Progress Management

report 1

The Project management report 1 was completedon time. See (Coyle, 2014)

Problems

Actual

Accessing Twitter API The decision has being made under advisementfrom project lecturers to duplicate the twitterfeeds using the Twilert application.Twilert provides a free service for accessing livetwitter feeds however it only delivers 100 RSSfeeds per day.The trial run lasts for 15 days so it will providethe project over 1500 tweets. These tweets willthen be duplicated to match the historic stockmarket prices.The stock market data provide daily end of dayprices.

Potential

The quality and quantity of

the Twitter data provide

by Twilert.

The Twitter data provided by Twilert must be ofgood quality and having enough data is essential.Data will be duplicated otherwise.




Raid Log:

Risks

Open

RisksDate last

reviewed

30/03

/2014

Risk

Ref

RiskCateg

ory

RiskDescription

Raisedby

Date

Identifie

d

Priorit

y

Impac

t

Pr ob

MitigationCategory

Mitigation

O wner

Update

Date

updated

EndDate

R01technol

ogy

No databackupavailable R.Coyle

10-Feb-14 H H L

prevention

Source

online

storage fordata. RC

10-Feb-14

R02 costAcquiring datafor free. R.Coyle

10-Feb-14 M M L

acceptance

Source freehistor

ictwitte

rfeeds. RC

10-Feb-14

R03 timeAcquiring dataon time. R.Coyle

10-Feb-14 M H H

prevention

Sours

e thedataon

time. RC

10-Feb-14

ClosedRisks

Risk

Ref

RiskCateg

ory

RiskDescription

Raisedby

Date

Identifie

d

Priorit

y

Impac

t

Pr ob

MitigationCategory

Mitigation

O wner

Update

Date

updated

EndDate

R01technol

ogy

No databackupavailable R.Coyle

17-Feb-14 H H L

prevention

Source

harddrivefor

storage RC

10- Jun-14

R02 cost

No costsneeded foruse of data R.Coyle

24-Mar-14 L L L

acceptance

Usingdifferent

data. RC

24-Mar-14

R03 time

Data will beaquired ontime. R.Coyle

24-Mar-14 M H H

contingenc

y

Sourse thedataon

time. RC

24-Mar-14




Assumptions

Assumptions The purpose of this document is to surface, document, analyse and monitor the key assumptions

upon which the plan is based. Planning parameters, design parameters, issues and risks will be generated from these assumptions Ref # Assumption Importance Certainty Influence Test

TestDate

A01

Lecturers will provideprompt feedback and

guidance 4 - critical 3 - Probable HSend request to test

level of response

10-Feb-14

A02

Twitter will repley to mygrant request for the useof their historic data. 2 - somewhat 1 - unknown L Wait for replay.

03-Mar-14

A03RSS feeds gathered fromtwitter not missing data. 3 - important 4 - Fact H Unknow as of yet.

30-Mar-14

A04

Skills developed for

analysis of data. 4 - critical 4 - Fact H

Continue arriving to

lectures.

03-Mar-

14

Issues

Issues are unexpected incidents or events

IssueRef

IssueDescription

Raisedby

DateRaised

Impact PriorityActionPlan

Status OwnerTarget

ResolutionDate

ActualResolution

Date

I01

Unexpectedissue inaccessingtwitter feeds. RC

17-Feb-14 H H

Identifydifferentmeans ofaccessing

the twitterfeeds. open RC 10-Feb-14

I02

Twitter APIaccess morecomplex thananticipated. RC

03-Mar-14 H H

This issue

has beingbrought upto ProjectLeturers.Awaitingresponse. closed RC 03-Mar-14 24-Mar-14

I03

No responsefrom Twitterdeveloperdata grantscheme. RC

24-Mar-14 H M

This issuehas being

brought upto ProjectLeturers.

Alternativesolutionhas beingprovided. closed RC 24-Mar-14 30-Mar-14




Dependency

Depen

dencyDependency

RefProjec

tDependencyDescription

Raisedby

DateRaised

Impac

t

Priorit

y

Period

Affected

Action

Plan

Owner

Target

ResolutionDate

Actual

ResolutionDate

D01NCIFacilities

IT facilities availablefor running twitterAPI RC

10-Feb-14 H H

Feb -Mar

Conf irm

availabilitywithIT RC

Mar-14

Mar-14

D02ExternalExpert

Twitter historicaldata grant approval. RC

03-Mar-14 L L

Mar-Apr

Awaitingresponse

fromtwitterfor

historicaldatagrantapproval. RC

Mar-14

Mar-14

D03ExternalExpert

Aquire Twitter datafrom Twilert. RC

30-Mar-14 M H

Mar-Apr

Awaitingresponsefromexter

nalclient

. RCApr-14

Products due for completionBy the next period the following should be accomplished.

Gathering of Twitter feeds. Should have gathered all twitter data in relation

to Samsung.Gathering of stock market

data.

Should have gathered all Nasdaq data in relationto Samsung.

Analysis of data. Once all data has being gathered analysis cantake place.

Projects write up. Commenced first draft.Management Progress

Report 3.

This report will be the end of this period.




ConclusionThis project is still on course for completion within the requested timeline.The project data source has changed since there has being no replay from theTwitter research data grant scheme to access their historical data.Twilert will now provide the data for the project.It has proven to be a reliable source but can only provide access to 100 RSS feedsper day, this data however will be duplicated providing enough data to completethe project.Yahoo finance will provide the historical stock market prices.

Alternatives:

If the Twitter developer grant is approved within the next 2 weeks theproject can revert back to using the correct historical data.


Document Location


Revision History


Revisiondate



20/04/14 First Issue

Approvals

This project requires the following approvals.

Name Signature Title Date of issue VersionRobert Coyle Project Manager 20/04/14 1

Distribution






Purpose of Document

The purpose of this document is to provide the project lecturer, Oisin Creaner,with a summary of the status of the project.

Date of report20/04/14

Period covered

1/04/14 – 20/04/14

Schedule Status


Updated Gantt chart


Term DefinitionAPI Application programming interface





Acquired Stock Data This was completed on the 20-04-14.

74

75

15

725

77

7

03-Feb 23-Feb 15-Mar 04-Apr 24-Apr 14-May 03-Jun

Project Proposal

Create Python codes








Acquired Twitter Data This was completed on the 20-04-14.

Problems

Actual

Analysis of Data The decision has being made to use companiesin the same stock market.The three brands I have chosen are on theNASDAQ stock exchange. This has mitigated theproblems that would have being encounteredwith different currency and time frames that areassociated with foreign stock exchanges.

Potential

Cleaning Twitter Data Cleaning of Twitter data acquired from Javascript can be completed in the short time framethat is left.

Raid Log:

Risks

Open Risks Date last reviewed 20/04/2014

Risk Ref Risk Category Risk Description Raised by Date Identified Priority Impact ProbMitigation

Category

R01 technology No data backup available R.Coyle 10-Feb-14 H H L prevention

R02 cost Acquiring data for free. R.Coyle 10-Feb-14 M M L acceptance

R03 time Acquiring data on time. R.Coyle 10-Feb-14 M H H prevention

R04 time Data analysis. R.Coyle 20-Apr-14 H H M prevention

Mitigation Owner Update Date updated End Date

Source online storage for data. RC 10-Feb-14

Source free historic twitter feeds. RC 10-Feb-14

Sourse the data on time. RC 10-Feb-14

Perpare and analyze data. RC 21-Apr-14




Assumptions

Assumptions The purpose of this document is to surface, document, analyze and monitor the keyassumptions upon which the plan is based. Planning parameters, design parameters, issues and risks will be generated fromthese assumptions

Ref # Assumption Importance Certainty Influence Test Test Date

A01

Lecturers willprovide promptfeedback andguidance 3 - important 3 - Probable M

Send request to testlevel of response

10-Feb-14

A04

Skills developedfor analysis ofdata. 4 - critical 4 - Fact H

Continue arriving tolectures.

03-Mar-14

A05

Data can becleaned and

prepared foranalysis. 4 - critical 4 - Fact H

Project lectures can

assist during lecturehours.

20-Apr-14

A05

Cleaned data isadequate and canbe analyzed 4 - critical 4 - Fact H

Project lectures canassist during lecture

hours.20-Apr-14

Issues

Issue Ref Issue Description Raised by Date Raised Impact Priority

I01 Unexpected issue in accessing twitter feeds. RC 17-Feb-14 H H

I02Twitter API access more complex thananticipated. RC 03-Mar-14 H H

I03The Response from the Twitter developerdata grant scheme came back rejected. RC 24-Mar-14 L L

Action Plan Status OwnerTarget

ResolutionDate

ActualResolution

Date

Data was acquired. closed RC 10-Feb-14 20-Apr-14

This issue has being brought up to Project Lecturers.Awaiting response. closed RC 03-Mar-14 24-Mar-14

This issue has being brought up to Project Lecturers.Alternative solution has being provided. closed RC 24-Mar-14 20-Apr-14

Closed RisksRisk Ref Risk Category Risk Description Raised by Date Identified Priority Impact Prob

R01 technology No data backup available R.Coyle 17-Feb-14 H H L

R02 cost No costs needed for use of data R.Coyle 24-Mar-14 L L L

R03 time Data is acquired. R.Coyle 24-Mar-14 M H H

Mitigation

CategoryMitigation Owner Update Date updated End Date

prevention Source hard drive for storage RC 10-Jun-14

acceptance Using different data. RC 24-Mar-14

contingency Sourse the data on time. RC 20-Apr-14 20-Apr-14




Dependency

Dependency Ref

Project

Dependency

Description

Raisedby

DateRaise

d

Impact

Prior ity

Period

Affected

Action

Plan

Owner

TargetResolut

ionDate

ActualResolut

ionDate

D01NCIFacilities

IT

facilitiesavailableforrunningtwitterAPI RC

10-Feb-14 H H

Feb -Mar

Confirm

availability

withIT RC Mar-14 Mar-14

Products due for completion

By the next period the following should be accomplished.

Cleaning of Twitter data. Twitter data will be cleaned and time seriesprepared for analysis.

Cleaning of stock market

data.

Stock data will be cleaned and time seriesprepared for analysis, Stock market data timeseries is per day.

Analysis of data. Once all data has being and cleaned analysis willbegin.

Projects write up. Commenced first draft.

ConclusionThis project is still on course for completion within the requested timeline.The project data source has changed since the Twitter Historical Data grant wasdenied. I now have gathered a weeks worth of Twitter data associated to threecompanies that are on the same stock exchange.I will now focus on Apple Inc., Tesla Motors, Inc. and Microsoft Corporation.

These tech companies being on the same stock exchange (NASDAQ) will create amore straightforward approach to the analysis. Samsung Electronics, which wasmy original company I had selected to base the analysis upon, is on the Koreanstock market. Not only would I have different time series but I would also have tomodify the currency difference.Yahoo finance will provide the historical stock market prices.I am hoping to find a correlation between the twitter activity and the stockmarket prices of the three brands with a lag of around three to four days.

Alternatives:

If I can gather the stock market prices in hourly format the analysis wouldbe more detailed.




References

Usefulsocialmedia.com. 2014. Twitter Evolves – Becoming more brand friendly |

Useful Social Media. [online] Available at:http://www.usefulsocialmedia.com/measurement/Twitter-evolves-–-becoming-more-brand-friendly [Accessed: 9 Feb 2014].

Johnson, L. 2014. Samsung Galaxy S5 release date, news, rumours, specs and price -

News - Trusted Reviews. [online] Available at:http://www.trustedreviews.com/news/Samsung-galaxy-s5-release-date-news-rumours-specs-and-price [Accessed: 9 Feb 2014].

Macrumors.com. 2014. Apple Continues to Lose Smartphone Share, Gain Mobile

Phone Share in 4Q 2013. [online] Available at:http://www.macrumors.com/2014/01/28/apple-phone-share-4q-2013/[Accessed: 9 Feb 2014].

Wellclever.com. 2014. Well Clever - Publisher Centric Platforms. [online] Availableat: http://wellclever.com [Accessed: 9 Feb 2014].

usefulsocialmedia. 2014. Twitter Evolves -Becoming more brand friendly.[ONLINE] Available at:http://www.usefulsocialmedia.com/measurement/Twitter-evolves-–-becoming-more-brand-friendly. [Accessed 23 February 14].

btimes.co.uk. 2013. Samsung's $14bn is 'Biggest Marketing Budget in History.[ONLINE] Available at: http://www.ibtimes.co.uk/samsung-14bn-marketing-budget-biggest-history-525979. [Accessed 28 February 14].

comprendia. 2014. If A Tweet Falls In The Forest? Maximizing TwitterEngagement Through Time Of Day Analysis. [ONLINE] Available at:http://comprendia.com/2012/07/17/if-a-tweet-falls-in-the-forest-maximizing-twitter-engagement-and-exposure-through-time-of-day-analysis/. [Accessed 24February 14].

powerpivotblog. 2013. Analyze a Twitter feed with Excel 2013, DataExplorer andGeoFlow. [ONLINE] Available at: http://www.powerpivotblog.nl/analyze-a-twitter-feed-with-excel-2013-dataexplorer-and-geoflow/. [Accessed 24February 14].

evolutionanalytics. 2013. What does Barack Obama tweet about most?. [ONLINE]Available at: http://blog.revolutionanalytics.com/2013/11/what-does-barack-obama-tweet-about-most.html. [Accessed 24 February 14].

skilledup. 2013. 50+ (Mostly) Free Excel Add-Ins For Any Task. [ONLINE]

Available at: http://www.skilledup.com/learn/business-entrepreneurship/mostly-free-excel-add-ins/. [Accessed 24 February 14].



datamachines. 2012. Decomposing North Carolina Amendment 1 with R andTableau (part 1). [ONLINE] Available at:http://datamachines.blogspot.ie/2012/05/decomposing-north-carolina-

amendment.html. [Accessed 24 February 14].

Twilert. 2014. Twitter search alerts. [ONLINE] Available at:http://www.twilert.com. [Accessed 10 March 14].

Twitter. 2014. Overview: Version 1.1 of the Twitter API. [ONLINE] Available at:https://dev.twitter.com/docs/api/1.1/overview. [Accessed 10 March 14].

Twitter. 2014. Data Grants. [ONLINE] Available at:https://engineering.twitter.com/research/data-grants. [Accessed 10 March 14].

Yahoo Finance, 2014. Samsung Electronics Co. Ltd. [ONLINE] Available at:http://finance.yahoo.com/q/hp?s=005930.KS+Historical+Prices. [Accessed 30March 14].

Twilert, 2014. Twitter search alerts. [ONLINE] Available at:http://www.twilert.com. [Accessed 10 March 14].

Yahoo Finance - Business Finance, Stock Market, Quotes, News (2014) YahooFinance. Available at: http://finance.yahoo.com (Accessed: 20 April 2014).

http://www.twilert.com/

http://www.twilert.com/