Exploiting emoticons in sentiment analysis

Abstract

As people increasingly use emoticons in text in order to ex- press, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoti-cons typically convey sentiment and demonstrate how we can exploit this by using a novel, manually created emoticon sentiment lexicon in order to improve a state-of-the-art lexicon-based sentiment classication method. We evaluate our approach on 2,080 Dutch tweets and forum mes- sages, which all contain emoticons and have been manually annotated for sentiment. On this corpus, paragraph-level accounting for sentiment implied by emoticons signicantly improves sentiment classication accuracy. This indicates that whenever emoticons are used, their associated senti- ment dominates the sentiment conveyed by textual cues and forms a good proxy for intended sentiment