IITB-Sentiment-Analysts: Participation in Sentiment Analysis in Twitter SemEval 2013 Task

Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 495–500, Atlanta, Georgia, June 14-15, 2013. c⃝2013 Association for Computational Linguistics IITB-Sentiment-Analysts: Participation in Sentiment Analysis in Twitter SemEval 2013 Task Karan Chawla, Ankit Ramteke, Pushpak Bhattacharyya Dept. of Computer Science and Engineering, IIT Bombay {chawlakaran,ankitr,pb}@cse.iitb.ac.in Abstract We propose a method for using discourse rela- tions for polarity detection of tweets. We have focused on unstructured and noisy text like tweets on which linguistic tools like parsers and POS-taggers don’t work properly. We have showed how conjunctions, connectives, modals and conditionals affect the sentiments in tweets. We have also handled the commonly used ab- breviations, slangs and collocations which are usually used in short text messages like tweets. This work focuses on a Web based application which produces results in real time. This ap- proach is an extension of the previous work (Mukherjee et al. 2012). 1. Introduction Discourse relation is an important component of natural language processing which connects phrases and clauses together to establish a cohe- rent relation. Linguistic constructs like conjunc- tions, connectives, modals, conditionals and ne- gation do alter the sentiments of a sentence. For example, the movie had quite a few memorable moments but I still did not like it. The overall polarity of the sentence is negative even though it has one positive and one negative clause. This is because of the presence of the conjunction but which gives more weightage to the clause fol- lowing the conjunction. Traditional works in discourse analysis use a discourse parser (Marcu et al., 2003; Polanyi et al., 2004; Wolf et al., 2005; Welner et al., 2006; Narayanan et al., 2009; Prasad et al., 2010). Many of these works and some other works in discourse (Taboada et al., 2008; Zhou et al., 2011) build on the Rhetorical Structure Theory (RTS) proposed by Mann et al. (1988) which tries to identify the relations between the nucleus and satellite in the sentence. Most of the work is based on well-structured text and the methods applied on that text is not suita- ble for the discourse analysis on micro-blogs because of the following reasons: 1. Micro-blogs like Twitter restricts a post (tweet) to be of only 140 characters. Thus, users do not use formal language to discuss their views. Thus, there are abundant spelling mis- takes, abbreviations, slangs, collocations, discon- tinuities and grammatical errors. These differences cause NLP tools like POS- taggers and parsers to fail frequently, as these tools are built for well-structured text. Thus, most of the methods described in the previous works are not well suited for discourse analysis on Micro-blogs like text. 2. The web-based applications require a fast response time. Using a heavy linguistic re- source like parsing increases the processing time and slows down the application. Most of the previous work on discourse analysis does not take into consideration the conjunc- tions, connectives, modals, conditionals etc and are based on bag-of-words model with features like …

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