Research On Chinese Phrase Sentiment Analysis Based On Chunking

With the rapid development of Internet, particularly the popularity of subjective media, such as forums and blog, etc, the strict boundaries between information distributors and receivers has been broken. Text is becoming one of the most important interaction ways, the subjective information contained in it has drawn increasing attention from companies and governments. And this change has made textural information explosive growth. Text sentiment analysis is a method to obtain subjective information automatically, which has become a hotpot in natural language processing.Text sentiment polarity analysis means analyzing the speakers and writers'attitude (or point of view, emotion), that is, analyzing the subjectivity information of text. Word sentiment analysis is the foundation of text sentiment analysis, it plays a important role. As a bridge between words and sentences, phrase can increase granularity of text sentiment analysis. Therefore phrase sentiment analysis has profound significance.In current research, one disadvantage of methods based on lexicons is that it is to tag words priori sentiment polarity: out of context. This paper presents a method to obtain the contextual sentiment polarity of words. For the lack of contextual corpus for sentiment analysis, we combine maximum entropy based cross-validation and manual annotation to construct the corpus. Then a valid set of contextual features is extracted to predict the word contextual sentiment polarity in the context. Compared with the methods based on lexicons, experiments show that F score is improved by 4.9%.For the two-word phrases sentiment analysis, this paper used rule-based method. We constructed templates, and used PMI (Pointwise Mutual Information) to obtain the sentiment polarity of phrases. Besides, this paper describes the function and the collection methods of adverbs of degree and negative words in phrase sentiment analysis.For more common phrase sentiment analysis, this paper used classification method to solve it. This paper constructed feature set including word sentiment polarity and chunk type, etc, and used maximum entropy model and support vector machine as the classification algorithms. Compared to the method based on the summation of polarity, the support vector machine obtained the best result.At last, we obtain sentence sentiment polarity by words and phrases respectively. The result shows that phrases increase granularity of sentiment polarity analysis and improve the performance of sentence sentiment polarity analysis.This paper obtained phrase sentiment polarity based on above techniques. The research was divided into two levels, namely contextual sentiment polarity disambiguation of Chinese words and phrase sentiment analysis. Experiments on sentence sentiment analysis show that the approach in this paper achieves a good result.