Sentiment Analysis in Twitter with Lightweight Discourse Analysis

Proceedings of COLING 2012: Technical Papers, pages 1847–1864, COLING 2012, Mumbai, December 2012. Sentiment Analysis in Twitter with Lightweight Discourse Analysis Subhabrata Mukherjee†, Pushpak Bhattacharyya‡ †IBM India Research Lab ‡Dept. of Computer Science and Engineering, IIT Bombay subhabmu@in.ibm.com, pb@cse.iitb.ac.in ABSTRACT We propose a lightweight method for using discourse relations for polarity detection of tweets. This method is targeted towards the web-based applications that deal with noisy, unstructured text, like the tweets, and cannot afford to use heavy linguistic resources like parsing due to frequent failure of the parsers to handle noisy data. Most of the works in micro-blogs, like Twitter, use a bag-of-words model that ignores the discourse particles like but, since, although etc. In this work, we show how the discourse relations like the connectives and conditionals can be used to incorporate discourse information in any bag-of-words model, to improve sentiment classification accuracy. We also probe the influence of the semantic operators like modals and negations on the discourse relations that affect the sentiment of a sentence. Discourse relations and corresponding rules are identified with minimal processing - just a list look up. We first give a linguistic description of the various discourse relations which leads to conditions in rules and features in SVM. We show that our discourse-based bag-of-words model performs well in a noisy medium (Twitter), where it performs better than an existing Twitter-based application. Furthermore, we show that our approach is beneficial to structured reviews as well, where we achieve a better accuracy than a state-of-the-art system in the travel review domain. Our system compares favorably with the state-of-the-art systems and has the additional attractiveness of being less resource intensive. KEYWORDS : Sentiment Analysis, Discourse, Twitter, Connectives, Micro-blogs 1847 1 INTRODUCTION An essential phenomenon in natural language processing is the use of discourse relations to establish a coherent relation, linking phrases and clauses in a text. The presence of linguistic constructs like connectives, modals, conditionals and negation can alter sentiment at the sentence level as well as the clausal or phrasal level. Consider the example, “@user share 'em! i'm quite excited about Tintin, despite not really liking original comics. Probably because Joe Cornish had a hand in.” The overall sentiment of this example is positive, although there is equal number of positive and negative words. This is due to the connective despite which gives more weight to the previous discourse segment. Any bag-of-words model would be unable to classify this sentence without considering the discourse marker. Consider another example, “Think i'll stay with the whole 'sci-fi' shit. but this time...a classic movie.” The overall sentiment is again positive due to the connective but, which gives more weight to the following discourse segment. Thus it is of utmost importance to capture all these phenomena in a computational model. Traditional works in discourse analysis use a discourse parser (Marcu 2000; Zirn et al., 2011, Wellner et al.; 2007; Pitler et al., 2009; Elwell et al., 2008) or a dependency parser (Vincent et al., 2006). Many of these works …

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