Twitter for Sentiment Analysis: When Language Resources are Not Available

This research proposes to use Twitter, the most popular microblogging platform nowadays, to collect a dataset of emotional texts in French, and estimated affective norms of words to construct an affective lexicon, which is used for polarity classification of video game reviews.

Abstract

Affective lexicons are a useful tool for emotion studies as well as for opinion mining and sentiment analysis. Such lexicons contain lists of words annotated with their emotional assessments. There exist a number of affective lexicons for English, Spanish, German and other languages. However, only a few of such resources are available for French. A lot of human efforts are needed to build and extend an affective lexicon. In our research, we propose to use Twitter, the most popular microblogging platform nowadays, to collect a dataset of emotional texts in French. Using the collected dataset, we estimated affective norms of words to construct an affective lexicon, which we use for polarity classification of video game reviews. Experimental results show that our method performs comparably to classic supervised learning methods.