Building Sentiment Lexicons for All Major Languages

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 ar-ticles on 30 languages. Despite cul-tural difference and the intended neutrality of Wikipedia articles, our lexicons show an average sentiment correlation of 0.28 across all language pairs. 1

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