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Sentiment Analysis in Twitter

The document discusses a study on sentiment analysis in Twitter, highlighting its importance for understanding public opinion on various topics. It details a two-task approach: phrase-level and sentence-level sentiment analysis, using an SVM classifier and various feature extraction techniques to evaluate sentiment. Results show that combining baseline and feature-based models improves performance, with key features influencing sentiment prediction identified in the analysis.

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Sentiment Analysis Methods•Information Retrieval•
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Sentiment Analysis in Twitter Contributed by: Ayushi Dalmia (ayushi.Dalmia@research.iiit.ac.in) Mayank Gupta(mayank.g@student.iiit.ac.in) Arpit Kumar Jaiswal(arpitkumar.jaiswal@students.iiit.ac.in) Chinthala Tharun Reddy(tharun.chinthala@students.iiit.ac.in) Course: Information Retrieval and Extraction, IIIT Hyderabad Instructor: Dr. Vasudeva Varma Source Code: [http://github.com/mayank93/Twitter-Sentiment-Analysis] Demo: [http://10.2.4.49:8000/TSAA/]
What is Sentiment Analysis? • It is classification of the polarity of a given text in the document, sentence or phrase • The goal is to determine whether the expressed opinion in the text is positive, negative or neutral.
Positive Negative Neutral
Why is Sentiment Analysis Important? • Microblogging has become popular communication tool • Opinion of the mass is important • Political party may want to know whether people support their program or not. • Before investing into a company, one can leverage the sentiment of the people for the company to find out where it stands. • A company might want find out the reviews of its products
Using Twitter for Sentiment Analysis • Popular microblogging site • Short Text Messages of 140 characters • 240+ million active users • 500 million tweets are generated everyday • Twitter audience varies from common man to celebrities • Users often discuss current affairs and share personal views on various subjects • Tweets are small in length and hence unambiguous
Problem Statement The problem at hand consists of two subtasks: • Phrase Level Sentiment Analysis in Twitter : Given a message containing a marked instance of a word or a phrase, determine whether that instance is positive, negative or neutral in that context. • Sentence Level Sentiment Analysis in Twitter: Given a message, decide whether the message is of positive, negative, or neutral sentiment. For messages conveying both a positive and negative sentiment, whichever is the stronger sentiment should be chosen. The task is inspired from SemEval 2013 , Task 9 : 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
Approach SVM Classifier and Prediction Feature Extractor Preprocessing Tokeniser Tweet Downloader
• Tweet Downloader • Download the tweets using Twitter API • Tokenisation • Twitter specific POS Tagger developed by ARK Social Media Search • Preprocessing • Removing non-English Tweets • Replacing Emoticons by their polarity • Remove URL, Target Mentions, Hashtags, Numbers. • Replace Negative Mentions • Replace Sequence of Repeated Characters eg. ‘coooooooool’ by ‘coool’ • Remove Nouns and Prepositions Approach
• Feature Extractor • Polarity Score of the Tweet • Percentage of Capitalised Words • Number of Positive/Negative Capitalised Words • Number of Positive/Negative Hashtags • Number of Positive/Negative/Extremely Positive/Extremely Negative Emoticons • Number of Negation • Positive/Negative special POS Tags Polarity Score • Number of special characters : ?,!,* • Number of special POS • Classifier and Prediction • The features extracted are next passed on to SVM classifier. • The model built is used to predict the sentiment of the new tweets. Approach
Results A baseline model by taking the unigrams, bigrams and trigrams and compare it with the feature based model for both the sub-tasks Sub-Task Baseline Model Feature Based Model Baseline + Feature Based Model Phrase Based 62.24 % 77.33% 79.90% Sentence Based 52.54% 57.57% 58.36% Accuracy F1 Score Sub-Task Baseline Model Feature Based Model Baseline + Feature Based Model Phrase Based 76.27* 75.23 75.98 Sentence Based 55.70 59.86 60.55 *Classifies in positive classes only, hence high recall.
Conclusion • We investigated two kinds of models: Baseline and Feature Based Models and demonstrate that combination of both these models perform the best. • For our feature-based approach, feature analysis reveals that the most important features are those that combine the prior polarity of words and their parts-of-speech tags.