A Hybrid Sentiment Lexicon for Social Media Mining

摘要: 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 context, to target domain. This limitation, adversely affects the effectiveness of sentiment analysis algorithms. In this paper, we introduce idea distant-supervision learn domain-focused improve coverage context terms. We present weighted strategy integrate scores with generate hybrid lexicon. Evaluations on text show superior classification over either individual lexicons. A further comparative study typical machine learning approaches also confirms position. promising results our investigations into transferability distant-supervised three different media.