Research On Text Sentiment Classification

With the development of Internet and the increase of internet users, more and more information has been organized as the textural format. People release subjective information through internet which contains their opinion, attitude and judgment about merchandises and current affairs.Text sentiment polarity analysis is related to Computational Linguistics, Artificial Intelligence, Machine Learning, Information Retrieval, and Data Mining, etc. It has broad applications; as a result, text sentiment analysis has become a hotspot in natural language processing in recent years.Text sentiment polarity analysis is to analyze the writers'attitude (or point of view, emotion), that is, analyzing the subjective information of text. Sentiment analysis in general terms can be divided into four levels: word sentiment analysis, phrase sentiment analysis, sentence sentiment analysis, and text sentiment analysis. Word sentiment analysis deals with words which contain subjective information, for example: noun, verb, adjective, adverb, etc. It's the precondition and foundation of text sentiment analysis. Sentence sentiment analysis deals with sentences which contain context information. Text sentiment analysis analyzes text's subjective information as a whole.For the problem of feature extraction in sentiment classification, this paper extracts different kinds of features, including sentiment patterns, sentiment words, degree adverbs, negative adverbs, and word sequences, to analyze their influences toward sentiment classification in text and sentence levels. The experimental results show that sentiment words, degree adverbs and negative adverbs are most helpful, and the performance is obviously improved with the help of sentiment patterns and word sequences.For the feature selection of sentiment analysis, this paper uses different methods of feature selection, including DF, CHI, the combination of DF and CHI, IG, the combination of IG and GA, to analyze their influences toward sentiment classification in text and sentence levels. The experimental results show that the method of the combination of IG and GA performs best.