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Sentiment analysis on twitter
PresenterNITHISH J PRABHU4JN12IS066Information Science & Engineering
Guided ByMrs. G. V. SOWMYAAssistant ProfessorInformation Science & Engineering
CONTENT
INTRODUCTION
WHY TWITTER
SYSTEM ARCHITECTURE
SCORING MODULE
SENTIMENT SCORING
CONCLUSION
INTRODUCTION
Understanding people is difficult.
Sentimental Analysis involves user’s attitude towards particular topic
-- positive -- negative -- neutral
WHY NEEDED ?
• Promotion: is this review positive or negative?
• Products: what do people think about the new iPhone?
• Politics: what do people think about this candidate or issue?
• Prediction: predict election outcomes or market trends from sentiment
MICROBLOGGING
Message Length: Tweets message is 140 characters.
Writing technique: The occurrence of incorrect spellings and cyber slang.
Availability: The amount of data available is immense.
Topics: Twitter users post messages about a range of topics.
TWITTER TERMINOLOGY
tweet re-tweet mention trends
User Tweet Twitter APIRemoval of
URL, @tags, #tags
Spell Correction
Emoticon Tagger POS Tagger
Transaction File• Emoticons• Adjective• Adverb• Verb
Scoring Module• Corpus
Based• Dictionary
BasedTweet
Sentiment Score
SYSTEM ARCHITECTURE
EMOTICONS STRENGTH
SCORING MODULE
Corpus Based Approach – Adjective
Dictionary Based Approach – Verb & Adverb
CORPUS BASED APPROACH
Adjective used to qualify object and domain specific.
But conjoined adjective makes situation reverse.
Example: Honest ‘and’ peaceful – same orientation Talented ‘but’ Irresponsible – opposite orientation
CORPUS BASED APPROACH
Log Linear Regression Model with Linear Predictor
where X is Conjunction counts W is Weight vector
Similarity between is calculated by
Seed List are taken & Semantic scores will be assigned.
DICTIONARY BASED APPROACH
Adverb can also change meaning of Adjective.Example: This is not a good book;
Verb can also convey opinions.Example: love, hate;
Semantic orientation is calculated by Word Net & added to Seed List.
VERB & ADVERB STRENGTH
DICTIONARY BASED APPROACH - ALGORITHM
TWEET SENTIMENT SCORING To calculate the overall sentiment of the tweet,
average the strength of all opinion indicators as
EXAMPLE
Fraction of tweet in caps: BOOOORING Pc=1/18=0.055 Length of repeated sequence, BOOOORING, Ns=3 Number of Exclamation marks, !!!, Nx=3
EXAMPLE
The list of Adjective Groups: AG1=totally unprepared, AG2=not good, AG3=boring The list of Verb Groups:
VG1=hate The list of Emoticons:
E1 = :(, Ne1 = 2
EXAMPLE
Score of Adjective GroupS (AG1) = S (totally unprepared) =0.8*-0.5 == -0.4S (AG2) = S (not good) =-0.8*1= -0.8S (AG3) = S (boring) = 0.5*-0.25 = -0.125
Score of Verb GroupS (VG1) = S (hate) = 0.5*-1 = -0.5
TWEET SENTIMENT SCORING
Since, the score is Negative value, Tweet is considered as Negative tweet
FREQUENCY OF POSITIVE & NEGATIVE TWEETS
CONCLUSION
The proliferation of microblogging sites like Twitter offers an opportunity to create theories & technologies that mine for opinions.
Corpus Based & Dictionary Based approach help to find semantic orientation.
Better the understand, better the move.
ANY QUERIES
Thank you
