NATURAL LANGUAGE PROCESSING. Sentiment Analysis on Twitter

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1 NATURAL LANGUAGE PROCESSING Sentiment Analysis on Twitter Mentor: Prof. Amitabha Mukherjee By Rohit Kumar Jha Sakaar Khurana Department of Computer Science and Engineering, IIT Kanpur NLP CS671 11/18/2013 1

2 Introduction In the past decade, new forms of communication, such as microblogging and text messaging have emerged and become ubiquitous. While there is no limit to the range of information conveyed by tweets and texts, often these short messages are used to share opinions and sentiments that people have about what is going on in the world around them. We plan to work on the following task. This task was part of SEMEVAL 2013 challenge. The task: Given a message, classify 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. NLP CS671 11/18/2013 2

3 Motivation Tweets and texts are short: a sentence or a headline rather than a document. The language used is very informal, with creative spelling and punctuation, misspellings, slang, new words, URLs, and genre-specific terminology and abbreviations, such as, RT for "re-tweet" and #hashtags, which are a type of tagging for Twitter messages. Another aspect of social media data such as Twitter messages is that it includes rich structured information about the individuals involved in the communication. For example, Twitter maintains information of who follows whom and re-tweets and tags inside of tweets provide discourse information. Working with these informal text genres presents challenges for natural language processing beyond those typically encountered when working with more traditional text genres, such as newswire data. Modelling such structured information is important because: (i) it can lead to more accurate tools for extracting semantic information, and (ii) because it provides means for empirically studying properties of social interactions (e.g., we can study properties of persuasive language or what properties are associated with influential users). NLP CS671 11/18/2013 3

4 Previous work Bag of Words Model Use a word list where each word has been scored positivity/negativity or sentiment strength Overall polarity determined by the aggregate of polarity of all the words in the text In SEMEVAL 2013, Team IITB used Bag of Words model with Discourse Information and able to achieve an accuracy of 39.80% Naive Bayesian Classifier Straightforward and frequently used method for supervised learning Maximum entropy classifiers are commonly used as alternatives to Naïve Bayesian classifier because they do not require statistical independence of the features that serve as predictors In SEMEVAM 2013, Team uottawa used Naïve Bayesian Classifier were able to achieve an accuracy of 42.51% Support Vector Machine In SEMEVAL 2013, Team NRC-Canada used SVM model with unigram, bigram, POS tags, negation etc as features and were able to achieve an accuracy of 69.02% NLP CS671 11/18/2013 4

5 Our Work Simple and Naive implementation of Bag of Words Model Incorporate the effect of modifiers like "very", "too", etc SVM implementation with unigrams+bigrams, negation, POS tagging, etc as features Incorporate the use of emoticons Incorporate the effect of #hashtags Use Bag of Words Model with SVM Bag of Words model with Discourse Information Sentence Weightage assignment in Bag of Words Model Take care of misspellings, informal use of words like happpppyyy, abbreviations, etc NLP CS671 11/18/2013 5

6 Naive Implementation of Bag of Words Important Points Use a word list where each word has been scored positivity/negativity or sentiment strength Overall polarity determined by the aggregate of polarity of all the words in the text Major polarity words have been given polarity between -4 to 4 depending on their polarity, like excellent is +4, but happy is +2, and so on Results Accuracy of ~42% when using Bag of Words Model without any special care Team IITB also used Bag of Words model but could achieve only ~40% accuracy. The major difference being probably that they considered only 0, +1, - 1 values of polarity of words. NLP CS671 11/18/2013 6

7 Bag of Words Model with features Important Features Incorporate the use of emoticons. If there are emoticons, clearly showing a particular emotion, don't proceed further Incorporate the use of Discourse Information along with the Bag of Words model Assigning double weightage to polarity words in sentences occurring later on in tweets. But it didn't make an overall difference Incorporate the effect of #hashtags. Apply Bag of Words model on the tweets after doing word boundary segmentation and if some clear sentiment appears, don't proceed ahead Incorporate the effect of modifiers like "very", "too", etc Results Achieved accuracy of ~56% after considering these features NLP CS671 11/18/2013 7

8 Important Features SVM Implementation POS: the number of occurrences for each part-of-speech tags. 'NN','VG','CD','JJ','CC','RB' pos tags are the only ones considered emoticons: - presence/absence of positive and negative emoticons at any position in the tweet; - whether the last token is a positive or negative emoticon; negation: the number of negated contexts. A negated context also affects the ngram and lexicon features: each word and associated with it polarity in a negated context become negated frequency: select top 1000 words and their presence Results Achieved accuracy of ~61% after considering these features NLP CS671 11/18/2013 8

9 SVM + Bag of Words Model Important Points Using a Hybrid Classifier using emoticons, Bag of Words Model and SVM Classifier If there is a clear presence of emoticons denoting the emotion of a tweets, simply use it Otherwise use the Bag of Words model and if there is a certain confidence level in the polarity reported, simply go with it If no clear and confident outcome obtained, use SVM classifier and the results from the first two steps to decide Results Achieved an accuracy of 68.32% with training on only around 8000 tweets, which is a really small training data NLP CS671 11/18/2013 9

10 Results Serial No. Methods Used Data Size Accuracy Obtained on SEMEVAL 2013 data 1. Bag of Words Model N.A % 2. Naïve Bayes Classifier 10,000 tweets 42.51% 3. Support Vector Machine 1.6 Million tweets 4. SVM + Bag of Words Model (Our result) 69.02% 8,000 tweets 68.32% NLP CS671 11/18/

11 References Subhabrata Mukherjee, Pushpak Bhattacharyya. Sentiment Analysis in Twitter with Lightweight Discourse Analysis Preslav Nakov, Sara Rosenthal,Zornitsa Kozareva,Veselin Stoyanov,Theresa Wilson. SemEval Task 2: Sentiment Analysis in Twitter Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Nicoletta Calzolari (chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, and Daniel Tapias, editors, Proceedings of the Seventh International Conference on Language Resources and Evaluation, LREC 10, pages , Valletta, Malta. NLP CS671 11/18/

12 Summary and Conclusion Objective Given a message, classify 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. Result Obtained Achieved an accuracy of 68.32% with training on only around 8000 tweets, which is a really small training data, compared to what was used by the team that came first Significance Our method achieves good accuracy with relatively small amount of data compared to that used by others Our method is targeted towards applications that cannot afford to use heavy processing Dataset Used We used the dataset provided by SEMEVAL 2013 for both training and testing purposes NLP CS671 11/18/

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