Experiments in Cross-Lingual Sentiment Analysis in Discussion Forums

One of the objectives of sentiment analysis is to classify the polarity of conveyed opinions from the perspective of textual evidence. Most of the work in the field has been intensively applied to the English language and only few experiments have explored other languages. In this paper, we present a supervised classification of posts in French online forums where sentiment analysis is based on shallow linguistic features such as POS tagging, chunking and common negation forms. Furthermore, we incorporate word semantic orientation extracted from the English lexical resource SentiWordNet as an additional feature. Since SentiWordNet is an English resource, lexical entries in the studied French corpus should be translated into English. For this purpose, we propose a number of French to English translation experiments such as machine translation and WordNet synset translation using EuroWordNet. Obtained results show that WordNet synset translation have not significantly improved the classification performance with respect to the bag of words baseline due to the shortage in coverage. Automatic translation haven't either significantly improved the results due to its insufficient quality. Propositions of improving the classification performance are given by the end of the article.