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

ACL ID P13-2090
Title Exploring Sentiment in Social Media: Bootstrapping Subjectivity Clues from Multilingual Twitter Streams
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

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.