Multi-lingual support for lexicon-based sentiment analysis guided by semantics

Abstract

Many sentiment analysis methods rely on sentiment lexicons, containing words and their associated sentiment, and are tailored to one specific language. Yet, the ever-growing amount of data in different languages on the Web renders multi-lingual support increasingly important. In this paper, we assess various methods for supporting an additional target language in lexicon-based sentiment analysis. As a baseline, we automatically translate text into a reference language for which a sentiment lexicon is available, and subsequently analyze the translated text. Second, we consider mapping sentiment scores from a semantically enabled sentiment lexicon in the reference language to a new target sentiment lexicon, by traversing relations between language-specific semantic lexicons. Last, we consider creating a target sentiment lexicon by propagating sentiment of seed words in a semantic lexicon for the target language. When extending sentiment analysis from English to Dutch, mapping sentiment across languages by exploiting relations between semantic lexicons yields a significant performance improvement over the baseline of about 29% in terms of accuracy and macro-level F1 on our data. Propagating sentiment in language-specific semantic lexicons can outperform the baseline by up to about 47%, depending on the seed set of sentiment-carrying words. This indicates that sentiment is not only linked to word meanings, but tends to have a language-specific dimension as well.