Sentiment Polarity Classification with Low-level Discourse-based Features
Authors: Evgeny A. Stepanov, Giuseppe Riccardi (Università di Trento, 2015)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
The poor state-of-the-art performances of discourse parsers prevent their application to downstream tasks. However, discourse parsing sub-tasks such as the detection of connectives and their sense classification have achieved satisfactory level of performance. In this paper we investigate the relevance of discourse connective features for tasks such as sentiment polarity classification. In the literature, discourse connectives are usually considered as modifiers of a polarity of a sentence or a word. In this paper we present experiments on using automatically extracted connectives and their senses as low-level features and as an approximation of a discourse structure for polarity classification of reviews. We demonstrate that, despite insignificant contributions to bag-of-words, the discourse-only models perform significantly above chance level.

Introduction
Discourse analysis has applications in many Natural Language Processing tasks; Webber et al. (2011) and Taboada and Mann (2006) among others list opinion mining, summarization, information extraction, essay scoring, etc. Availability of large discourse annotated resources such as Penn Discourse Treebank (PDTB) (Prasad et al., 2008a) and Rhetorical Structure Theory - Discourse Treebank (RST-DT) (Carlson et al., 2002) made it possible to develop statistical discourse parsers (e.g. (Marcu, 2000; Lin et al., 2014; Ghosh et al., 2011; Stepanov and Riccardi, 2013)). However, independent of the theory (RST or PDTB) the problem of end-to-end discourse parsing is far from being solved; thus, downstream application of these parsers yields mixed results. In this paper we focus on PDTB approach to discourse parsing, which can be roughly partitioned into detection of discourse relations, extractions of their argument spans and sense classification. In CoNLL 2015 Shared Task on Shallow Discourse Parsing (Xue et al., 2015) the best system (Wang and Lan, 2015) achieved F1 of 24 on the end-to-end parsing on a blind test set using strict evaluation that required exact match of all the spans and labels. Having such low end-to-end performances makes it difficult to apply PDTB-style discourse parsing to other NLP tasks. However, if we consider discourse parsing tasks individually, detection of discourse connectives and their classification into senses achieve high results: ≈ 90 for discourse connective detection and similarly ≈ 90 for connective sense classification (Stepanov et al., 2015). Thus, the output of these tasks could be used in other NLP applications. Discourse connectives are essentially function words and phrases. Function word frequencies is a popular feature in NLP tasks such as authorship detection (Kestemont, 2014), and it has also been applied to sentiment polarity classification (Abbasi et al., 2008). Resolving connective usage and sense ambiguities (Section 2), they are potentially able to provide more refined features than simple function word counts. On the other hand, grouping connectives with respect to their senses yields more coarse features. In this paper we explore the utility of these features for sentiment polarity classification of movie reviews (Pang and Lee, 2004).

Conclusion
We have described experiments on using lowlevel discourse-based features for sentiment polarity classification. The general observations are (1) discourse connectives in isolation generally significantly outperform the chance baseline; and (2) using even the most general top-level senses provides performance gains. This is particularly notable due to the fact that discourse connective detection and relation sense classification do not generalize well across domains (Prasad et al., 2011). Discourse connectives signal explicit discourse relations, which are only 53% of all discourse relations in PDTB. Implicit discourse relations (47%), which have the same senses, are much harder to deal with. Given the state of the art on implicit relation sense classification, detection and application of all the discourse relations is not yet possible. However, as indicated by the experiments on using n-grams of relation senses, even approximations can contribute.