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Sentiment Analysis for Twitter Priyanka Bajaj [email protected] Kamal Gurala [email protected] Faraz Alam [email protected] Ritesh Kumar Gupta [email protected] Guided By : Satarupa Guha [email protected]

Sentiment Analysis in Twitter

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Page 1: Sentiment Analysis in Twitter

Sentiment Analysis for Twitter

Priyanka Bajaj [email protected] Gurala [email protected] Alam [email protected] Kumar Gupta [email protected]

Guided By : Satarupa Guha [email protected]

Page 2: Sentiment Analysis in Twitter

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

Page 3: Sentiment Analysis in Twitter

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 better”

Page 4: Sentiment Analysis in Twitter

• An online social networking and micro blogging service

• Enables users to send and read "tweets", which are text messages limited to 140 characters, hence unambiguous

• 500 million tweets daily by 240+ million active users

• Audience varies from common man to celebrities

• Users discuss current affairs and share personal views

Our goal:To determine whether the expressed opinion in the tweets is positive, negative or neutral.

For tweets conveying both a positive and negative sentiment, choose the stronger sentiment

About

Page 5: Sentiment Analysis in Twitter

Natural Language Processing: The attempt to use programming to read and understand the meaning of text.

Semantic Analysis:

Use of Natural Language processing (NLP) to derive "sentiment," or subjective information from text.

Artificial Intelligence:Using information provided by NLP and mathematics to determine whether something is negative or positive

Glossary

Page 6: Sentiment Analysis in Twitter

Challenges• Tweets are highly unstructured and also non-grammatical

• Out of Vocabulary Words

• Lexical Variation

• Extensive usage of acronyms like asap, lol, afaik

Page 7: Sentiment Analysis in Twitter

Our System

Page 8: Sentiment Analysis in Twitter

• Tweet Downloader – Download the tweets using Twitter API

• Tokenisation – Twitter specific POS Tagger and tokenizer 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

Page 9: Sentiment Analysis in Twitter

• 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.

Page 10: Sentiment Analysis in Twitter

Results and Conclusion

A 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

Page 11: Sentiment Analysis in Twitter

1. Concepts of Data Mining and Information Retrieval

2. Python Language

3. Java, Eclipse

4. Support Vector Machine(SVM) Theory

5. LIBSVM package for accuracy and f-Measure

6. Twitter.inc API for training set

7. NLTK

8. Shell Script for integration

Tools and Technology Used

Page 12: Sentiment Analysis in Twitter

Thank You