Sentiment Analysis for Twitter

Priyanka Bajaj priyanka.bajaj@students.iiit.ac.inKamal Gurala kamal.gurala@students.iiit.ac.inFaraz Alam faraz.alam@students.iiit.ac.inRitesh Kumar Gupta ritesh.kumar.gupta@in.ibm.com

Guided By : Satarupa Guha satarupaguha11@gmail.com

AGENDA

1.Introduction – Sentiment Analysis

2.About Twitter and Our Goal

3.Glossary

4.Challenges

5.Approach

6.Results and Conclusion

7.Tools and Technologies

What is Sentiment Analysis?Mechanism to extract opinions, emotions and sentiments in text

Enable us to track attitudes and feelings on the web based on blog posts, comments, reviews and tweets on different topics

Enable to track products, brands and people and determine whether they are viewed positively or negatively on the web. 

           acts: "The painting was more expensive than a Monet"

           pinions: "I honestly don't like Monet, Pollock is the better” artist"

Challenges

• Tweets are highly unstructured and also non-grammatical

• Out of Vocabulary Words

• Lexical Variation

• Extensive usage of acronyms like asap, lol, afaik

Our System

• Tweet Downloader – Download the tweets using Twitter API

• Tokenisation – Twitter specific POS Tagger developed by ARK Social

Media Search• Preprocessing

– Replacing Emoticons by their polarity, assign scores– Remove URL, Target Mentions– Replace #text -> text, since hashtags may contribute to the

sentiment– Replace Sequence of Repeated Characters eg. ‘cooooool’

by ‘cool’ and assign higher score– Twitter specific stop word removal– Acronym expansion

System Details

• Feature Extractor

– Unigrams and Bigrams

– Polarity Score of the Tweet (f1)– Count of Positive/Negative Words (f2,f3)

– Maximum Positive/Negative Score for Words (f4,f5)

– Count of Positive/Negative Emoticons and assign scores(contibutes to all f1,f2,f3,f4,f5)

– Positive/Negative special POS Tags Polarity Score

• Classifier and Prediction

– Features extracted are fed into to SVM classifier

– Model built used to predict sentiment of new tweets

System Details Contd.

Results and ConclusionA baseline model by taking the unigrams, and compare it with the bigrams and lexicon features model

Sub-Task Baseline Model Feature Based Model

Sentence Based 49.81% 57.85%

Accuracy F1 Score (f-Measure)

Sub-Task Baseline Model Feature Based Model

Sentence Based 55.56 61.17

• We investigated two kinds of models: Baseline and Feature Based Models

• For our feature-based approach, feature analysis reveals that the most important features are bigrams and those that combine the prior polarity of words and their parts-of-speech tags

Thank You