Automatic Sentiment Monitoring of Specific Topics in the Blogosphere
The classification of a text according to its sentiment is a task of raising relevance in many applications, including applications re- lated to monitoring and tracking of the blogosphere. The blogosphere provides a rich source of information about products, personalities, tech- nologies, etc. The identification of the sentiment expressed in articles is an important asset to a proper analysis of this user-generated data. In this paper we focus on the task of automatic determination of the po- larity of blogs articles, i. e., the sentiment analysis of blogs. In order to identify whether a piece of text expresses a positive or negative opin- ion, an approach based on word spotting was used. Empirical results on different domains show that our approach performs well if compared to costly and domain-specific approaches. In addition to that, if we consider an aggregation of a set of documents and not the polarity of each indi- vidual document, we can achieve an accuracy distribution around 90% for specific topics of a certain domain.