Dynamic construction of dictionaries for sentiment classification

The sentiment classification is one of the new challenges emerged with the advence of social networks. Our purpose is to determine the sentimental orientation of a Facebook comment (positive or negative) by using the linguistic approach. In most of the sentiment analysis applications using this approach, the sentiment lexicon plays a key role. Thus, it is very important to create a lexicon covering several sentiment words. For this reason, we address in this paper the problem how to group and list words present in the corpus into two dictionaries. We proposed a new automatic technique to create the positive and negative dictionaries that exploits the emotions symbols (emoticons, acronyms and exclamation words) present in comments. More importantly, our idea allows to enlarge these dictionaries with an enrichment step. Finally, by using these prepared dictionaries, we predict the positive and negative polarities of the comment. We evaluate our approach by comparison to human classification. Our results are also effective and consistent.