Contextual Sentiment Analysis in Social Media Using High-Coverage Lexicon

Automatically generated sentiment lexicons offer sentiment information for a large number of terms and often at a more granular level than manually generated ones. While such rich information has the potential of enhancing sentiment analysis, it also presents the challenge of finding the best possible strategy to utilising the information. In SentiWordNet, negation terms and lexical valence shifters (i.e. intensifier and diminisher terms) are associated with sentiment scores. Therefore, such terms could either be treated as sentiment-bearing using the scores offered by the lexicon, or as sentiment modifiers that influence the scores assigned to adjacent terms. In this paper, we investigate the suitability of both these approaches applied to sentiment classification. Further, we explore the role of non-lexical modifiers common to social media and introduce a sentiment score aggregation strategy named SmartSA. Evaluation on three social media datasets show that the strategy is effective and outperform the baseline of using aggregate-and-average approach.