Exploiting emoticons in sentiment analysis

TLDR

As people increasingly use emoticons to express, stress, or disambiguate sentiment, automated sentiment analysis tools must correctly account for these graphical cues. The study analyzes how emoticons convey sentiment and demonstrates that a novel, manually created emoticon sentiment lexicon can improve a state‑of‑the‑art lexicon‑based sentiment classification method. The authors evaluate this lexicon‑based method on 2,080 Dutch tweets and forum messages that contain emoticons and have been manually annotated for sentiment. Paragraph‑level accounting for emoticon sentiment significantly improves classification accuracy, indicating that emoticons dominate textual cues and serve as a reliable proxy for intended sentiment.

Abstract

As people increasingly use emoticons in text in order to express, 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 emoticons 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 classification method. We evaluate our approach on 2,080 Dutch tweets and forum messages, which all contain emoticons and have been manually annotated for sentiment. On this corpus, paragraph-level accounting for sentiment implied by emoticons significantly improves sentiment classification accuracy. This indicates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual cues and forms a good proxy for intended sentiment.