Sentiment Mining through Mixed Graph of Terms

The spread of social networks allows sharing opinions on different aspects of life and daily millions of messages appear on the web. This textual information can be divided in facts and opinions. Opinions reflect people's sentiments about products, personalities and events. Therefore this information is a rich source of data for opinion mining and sentiment analysis: the computational study of opinions, sentiments and emotions expressed in a text. Its main aim is the identification of the agreement or disagreement statements that deal with positive or negative feelings in comments or reviews. In this paper, we investigate the adoption of a probabilistic approach based on the Latent Dirichlet Allocation (LDA) as Sentiment grabber. By this approach, for a set of documents belonging to a same knowledge domain, a graph, the Mixed Graph of Terms, can be automatically extracted. The paper shows how this graph contains a set of weighted word pairs, which are discriminative for sentiment classification. The proposed method has been tested on standard datasets and for the real-time analysis of tweets of opinion holders in various contexts. The experimental evaluation shows how the proposed approach is effective and satisfactory.