Paper: Building Sentiment Lexicons for All Major Languages

ACL ID P14-2063
Title Building Sentiment Lexicons for All Major Languages
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

Sentiment analysis in a multilingual world remains a challenging problem, be- cause developing language-specific senti- ment lexicons is an extremely resource- intensive process. Such lexicons remain a scarce resource for most languages. In this paper, we address this lexicon gap by building high-quality sentiment lexi- cons for 136 major languages. We in- tegrate a variety of linguistic resources to produce an immense knowledge graph. By appropriately propagating from seed words, we construct sentiment lexicons for each component language of our graph. Our lexicons have a polarity agreement of 95.7% with published lexicons, while achieving an overall coverage of 45.2%. We demonstrate the performance of our lexicons in an extrinsic analysis of 2,000 distinct historical figures? Wikipedia a...