Technology Research On Sentiment Analysis For Chinese Web Reviews

With the rapid development of Web technology, the Web has become a very important source from which more and more people obtain information. In the meanwhile, it is becoming a significant platform for people to express their viewpoints. Mining and analyzing this rapidly expanding information on web, especially the sentiment of the online reviews posted by users, can better our understanding of the consuming habits and public opinions of various users. Besides, it plays a crucial role in decision-making for many institutions, such as enterprises, the government, etc.At the beginning, this paper introduces the background of sentiment analysis and its prospect, and describes the conception and features of Chinese Web reviews. And then, according to the process of sentiment reviews for Web reviews, this paper makes a research in the approach of gathering and preprocessing Web reviews, and the technology of sentiment analysis. For sentiment analysis, this paper researches two methods based on text classification and sentiment dictionary respectively.The biggest value of sentiment analysis is generating summaries from many reviews which focus on the same topic, so this refers to how to get large numbers of reviews spreading on the Web. Generally, the reviews on one topic are distributed intensively on several Websites and Web pages in the same Website are highly structured. So this paper design a real-time Web page processing technique based on Message-Oriented Middleware aimed at parallel downloading and preprocessing Web pages, which gets the reviews data for sentiment analysis.Then, this paper proposes two approaches for sentiment analysis. Firstly, based on text classification technology, we propose a joint feature selection method based on relevance and redundancy to eliminate redundant features, find significant features for classification and consequently improve the accuracy of text sentiment classification, and then the well known classification technique, support vector machine, is used to classify the sentiment polarity. Secondly, based on sentiment dictionary technology, we utilize HowNet to construct a sentiment dictionary which is used to compute the sentiment orientation of words and phrases in the reviews. And then, the sentiment orientation of phrases is summed to compute the sentiment orientation of reviews.Finally, we use these two proposed methods to analyze the sentiment orientation of the public data set, as well as the data sets collected in this research. The experimental results show that the feature selection method and the sentiment dictionary based sentiment analysis method proposed in this paper are effective, and the sentiment dictionary based method outperforms the text classification based method.