Sentimental Analysis of twitter data .
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Sentiment analysis is used to classify text as expressing positive, negative, or neutral sentiment. It is important for understanding public opinion on social media, where people share views on topics like politics and products. This document discusses using sentiment analysis on Twitter data, which presents challenges due to its informal, unstructured nature. An approach is described that downloads tweets, preprocesses the text by removing noise, extracts sentiment-related features, and uses an SVM classifier to determine sentiment at the phrase and sentence level. Evaluation shows that combining unigram/bigram features with the sentiment features provides the best accuracy, achieving up to 79.9% for phrase-level and 60.55% for sentence-level sentiment classification.
Sentimental Analysis of twitter data .
- 1. Sentiment Analysis in Twitter Contributed by: ANAND KUMAR MISHRA(141321300X) ABHIJEET PANDEY(141321300X) SURENDRA PRATAP(__________) Instructor: Mr. Sudhanshu Saurabh
- 2. 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.
- 4. 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
- 5. 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
- 6. 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.
- 7. Challenges • Tweets are highly unstructured and also non-grammatical • Out of Vocabulary Words • Lexical Variation • Extensive usage of acronyms like asap, lol, afaik
- 8. Approach SVM Classifier and Prediction Feature Extractor Preprocessing Tokeniser Tweet Downloader
- 9. • 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
- 10. • 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
- 11. 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.
- 12. 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.
- 13. Thank You