Exploring Sentiment in Social Media: Bootstrapping Subjectivity Clues from Multilingual Twitter Streams

Abstract

We study subjective language in social media and create Twitter-specific lexi-cons via bootstrapping sentiment-bearing terms from multilingual Twitter streams. Starting with a domain-independent, high-precision sentiment lexicon and a large pool of unlabeled data, we bootstrap Twitter-specific sentiment lexicons, us-ing a small amount of labeled data to guide the process. Our experiments on English, Spanish and Russian show that the resulting lexicons are effective for sentiment classification for many under-explored languages in social media. 1