Lingmotif-lex: a Wide-coverage, State-of-the-art Lexicon for Sentiment Analysis

Abstract

We present Lingmotif-lex , a new, wide-coverage, domain-neutral lexicon for sentiment analysis in English. We describe the creation process of this resource, its assumptions, format, and valence system. Unlike most sentiment lexicons currently available, Lingmotif-lex places strong emphasis on multi-word expressions, and has been manually curated to be as accurate, unambiguous, and comprehensive as possible. Also unlike existing available resources, Lingmotif-lex comprises a comprehensive set of contextual valence shifters (CVS) that account for valence modification by context. Formal evaluation is provided by testing it on two publicly available sentiment analysis datasets, and comparing it with other English sentiment lexicons available, which we adapted to make this comparison as fair as possible. We show how Lingmotif-lex achieves significantly better performance than these lexicons across both datasets.