A probabilistic approach to Tweets’ Sentiment Classification

Prior to 2003, mankind generated a total of about 5 Exabyte’s of contents. Now, we generate this amount of contents in about two days! The spread of generic (as Twitter, Facebook or Google+) or specialized (as LinkedIn or Viadeo) social networks allows sharing opinions on different aspects of life every day. Therefore this information is a rich source of data for opinion mining and sentiment analysis. This paper introduces a novel approach to the sentiment analysis based on the Weighted Word Pairs obtained by the use of the Latent Dirichlet Allocation (LDA) approach. The proposed methodology aims at identifying a word-based graphical model for depicting and mining a positive or negative attitude towards a topic. For the evaluation of the proposed approach a challenging scenario has been set: the real-time analysis of tweets. The experimental evaluation shows how the proposed approach is effective and satisfactory