Sentiment Classification Based on Random Process
Jintao Mao, Jian Zhu · 2012
Sentiment classification has attracted increasing interest from Natural Language Processing. The goal of sentiment classification is to automatically identify whether a given piece of text expresses positive or negative opinion towards a topic of interest. We present the standpoint that uses a human model based on random process to determine text polarity classification. Experiment results showed that on movie review corpus, the human modeling approach has a relatively higher accuracy than that of SVMs and Naïve Bayes classifier.