JU_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines

Proceedings of SemEval-2016, pages 440–444, San Diego, California, June 16-17, 2016. c⃝2016 Association for Computational Linguistics JU NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines Braja Gopal Patra, Dipankar Das, and Sivaji Bandyopadhyay Department of Computer Science and Engineering, Jadavpur University Kolkata, India {brajagopal.cse,dipankar.dipnil2005}@gmail.com sivaji cse ju@yahoo.com Abstract We describe the system submitted to the SemEval-2016 for detecting stance in tweets (Task 6, Subtask A). One of the main goals of stance detection is to automatically determine the stance of a tweet towards a specific tar- get as ‘FAVOR’, ‘AGAINST’, or ‘NONE’. We developed a supervised system using Support Vector Machines to identify the stance by an- alyzing various lexical and semantic features. The average F1 score achieved by our system is 60.60. 1 Introduction Recent research in the areas of opinion mining and/or sentiment analysis on natural language texts is gaining importance due to various academic and commercial perspectives. One of the main reasons is that a vital amount of information can be obtained from text data that are available in the internet in forms of news, reviews, blogs, chats, and tweets. Several experiments have been attempted in the field of sentiment analysis or opinion mining on user generated content or social media data till date (Pa- tra et al., 2015). One of the main goals in such tasks is to assign polarities (positive or negative) to a piece of text. Identification of the writer’s attitude towards a specific target, known as stance detection, is a relevant and challenging topic which has unduly been averted by most researchers (Mohammad et al., 2016). Lately it has become conventional that peo- ple express stance explicitly or implicitly in various microblogging sites. Stance detection is the task of automatically de- termining from text whether the author is in favor of the given target, against the given target, or whether neither inference is likely.1 The stance detection can often bring complementary information to sen- timent analysis, because we often care about the au- thor’s evaluative outlook towards specific targets and propositions rather than simply considering whether the speaker is angry or happy (Mohammad et al., 2016). Stance detection even becomes more difficult when it is performed on the short texts like tweets. The latter being an important sub-field of sentiment analysis/opinion mining. Automatic stance detec- tion can be used in several applications such as in- formation retrieval, text summarization, and textual entailment. More recent approaches to stance detection have been performed using linguistic rules on online debate dataset (Somasundaran and Wiebe, 2009; Hasan and Ng, 2013; Sridhar et al., 2014) and NPOV Corpus from Wikipedia (Recasens et al., 2013). To the best of our knowledge, not much computational attempts have been performed on tweets for stance detection. It is also observed that both the super- vised and unsupervised machine learning algorithms have been implemented for identifying the stance and several features like n-gram, frame semantic features, and dependency were used in stance detec- tion tasks (Somasundaran and Wiebe, …

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