Analysing domain suitability of a sentiment lexicon by identifying distributionally bipolar words

Abstract Contemporary sentiment analysis approaches rely heavily on lexicon based methods. This is mainly due to their simplicity, although the best empirical results can be achieved by more complex techniques. We introduce a method to assess suitability of generic sentiment lexicons for a given domain, namely to identify frequent bigrams where a polar word switches polarity. Our bigrams are scored using Lexicographers Mutual Information and leveraging large automatically obtained corpora. Our score matches human perception of polarity and demonstrates improvements in classification results using our enhanced contextaware method. Our method enhances the assessment of lexicon based sentiment detection algorithms and can be further used to quantify ambiguous words