Lost in Translations? Building Sentiment Lexicons using Context Based Machine Translation

In this paper, we propose a simple yet efective approach to au tomatically building sentiment lexicons from English sentiment lexicons using publi cly available online machine translation services. The method does not rely on any semanti c resources or bilingual dictionaries, and can be applied to many languages. We propos e to overcome the low coverage problem through putting each English sentiment wor d into diferent contexts to generate diferent phrases, which efectively prompts the m achine translation engine to return diferent translations for the same English sentimen t word. Experiment results on building a Chinese sentiment lexicon (available at https:// github.com/fannix/ChineseSentiment-Lexicon) show that the proposed approach signiic antly improves the coverage of the sentiment lexicon while achieving relatively high pr ecision.