A Hybrid Sentiment Lexicon for Social Media Mining

Abstract

Sentiment lexicon is a crucial resource for opinion mining from social media content. However, standard off-the-shelve lexicons are static and typically do not adapt, in content and context, to a target domain. This limitation, adversely affects the effectiveness of sentiment analysis algorithms. In this paper, we introduce the idea of distant-supervision to learn a domain-focused lexicon to improve coverage and sentiment context of terms. We present a weighted strategy to integrate scores from the domain-focused with the static lexicon to generate a hybrid lexicon. Evaluations of this hybrid lexicon on social media text show superior sentiment classification over either of the individual lexicons. A further comparative study with typical machine learning approaches to sentiment analysis also confirms this position. We also present promising results from our investigations into the transferability of this distant-supervised hybrid lexicon on three different social media.