Tweet Sarcasm Detection Using Deep Neural Network

Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2449–2460, Osaka, Japan, December 11-17 2016. Tweet Sarcasm Detection Using Deep Neural Network Meishan Zhang1, Yue Zhang2 and Guohong Fu1 1. School of Computer Science and Technology, Heilongjiang University, China 2. Singapore University of Technology and Design mason.zms@gmail.com, yue zhang@sutd.edu.sg, ghfu@hotmail.com Abstract Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual fea- tures to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features. 1 Introduction Sarcasm has received much research attention in linguistics, psychology and cognitive science (Gibbs, 1986; Kreuz and Glucksberg, 1989; Utsumi, 2000; Gibbs and Colston, 2007). Detecting sarcasm au- tomatically is useful for opinion mining and reputation management, and hence has received growing interest from the natural language processing community (Joshi et al., 2016a). Social media such as Twitter exhibit rich sarcasm phenomena, and recent work on automatic sarcasm detection has focused on tweet data. Tweet sarcasm detection can be modeled as a binary document classification task. Two main sources of features have been used. First, most previous work extracts rich discrete features according to the tweet content itself (Davidov et al., 2010; Tsur et al., 2010; Gonz´alez-Ib´anez et al., 2011; Reyes et al., 2012; Reyes et al., 2013; Riloff et al., 2013; Pt´aˇcek et al., 2014), including lexical unigrams, bigrams, tweet sentiment, word sentiment, punctuation marks, emoticons, quotes, character ngrams and pronun- ciations. Some of these work uses more sophisticated features, including POS tags, dependency-based tree structures, Brown clusters and sentiment indicators, which depend on external resources. Overall, ngrams have been among the most useful features. Second, recent work has exploited contextual tweet features for sarcasm detection (Rajadesingan et al., 2015; Bamman and Smith, 2015). Intuitively, the history behaviors for a tweet author can be a good indicator for sarcasm. Rajadesingan et al. (2015) exploit a behavioral approach to model sarcasm, using a set of statistical indicators extracted from both the target tweet and relevant history tweets. Bamman and Smith (2015) study the influences of tweet content features, author features, audience features and environment features, finding that contextual features are very useful for tweet sarcasm detection. So far, most existing sarcasm detection methods in the literature leverage discrete models. While on the other hand, neural network models have gained much attention for related tasks such as sentiment analysis and opinion extraction, achieving the best results (Socher et al., 2013; dos Santos and Gatti, …

Content truncated to 500 words