Maximum Entropy Model based on Feature Extraction for Sentiment Detection of Text

The rapid development of social media services has facilitated the communication of opinions through online news, blogs, post bar, microblogs/tweets, and so forth. This article concentrates on the mining of emotions evoked by newmaterials. Compared to the classical sentiment analysis by using the word-emotion lexicon in the text, we combine the word with emotion via the intensive feature functions. We propose a maximum entropy model based on the feature extraction for sentiment classification, which generates the probability of sentiments conditioned to news text. In addition, one effective feature extraction strategies are proposed to refine the original miscellaneous news text. Experimental evaluations using real-world data validate the effectiveness of the proposed model on sentiment classification of news text.