Packed Feelings and Ordered Sentiments: Sentiment Parsing with Quasi−compositional Polarity Sequencing and Compression

Abstract

Recent solutions proposed for sentence- and phrase-level sentiment analysis have reflected a variety of analytical and computational paradigms that include anything from naïve keyword spotting via machine learning to full-blown logical treatments, either in pure or hybrid forms. As all appear to succeed and fail in different aspects, it is far from evident which paradigm is the optimal one for the task. In this paper, we describe a quasi-compositional sentiment learning and parsing framework that is well-suited for exhaustive, uniform, and principled sentiment classification across words, phrases, and sentences. Using a hybrid approach, we model one fundamental logically defensible compositional sentiment process directly and use supervised learning to account for more complex forms of compositionality learnt from mere flat phrase- and sentence-level sentiment annotations. The proposed framework operates on quasi-compositional sentiment polarity sequences which succinctly capture the sentiment in syntactic constituents across different structural levels without any conventional n-gram features. The results obtained with the initial implementation are highly encouraging and highlight a few surprising observations pertaining to role of syntactic information and sense-level sentiment ambiguity.