Research Of Product Opinion Mining Algorithm For Web Texts

With the wide range of the Internet applications, Blog,BBS,Wiki and other Web sites appear in a large number of customer reviews for products or services. The paper aims at these Web texts, research of how to extracting product features and opinion words from texts, and then holds for a client to determine polarity of opinions. The methods in the paper have been proved the applicability by experiments;the relative developed system also has a good robustness. Our study is mainly as follows:1,Using network resources, we first adopt pattern matching extraction methods based on HTML tags to extract product features from specific WebPages and then establish a basic feature dictionary. Secondly, we crawls comment texts from search engines to extract opinion words, and then calculate the polarity of the words based on HowNet to construct a characteristic of colloquial sentiment lexicon.2,Use of Chinese Dependency Parsing Analysis, combination with other semantic properties, we extract new product features and expand the feature dictionary, and then based on the bipartite graph model, we take the feature words and opinion words to repeated co-training, finally, we write news feature words and opinion words into respect lexicon. At the same time, we write the matching feature and opinion words into new text in the way of binary group.3,We artificially construct negative word table, turning the table and extent of vocabulary words, and then define a rating model of sentiment words, scoring the sentiment word, then judge the polarity of the word, that is, the opinion or attitude of the reviewer.Through the above work, this paper presents the views of Web text mining, namely, extracted of the feature words and opinion words and the analysis of praise and abuse. And we established related resources. The paper finally explores how to achieve cross-domain; to a certain extent, we have been proved the feasibility of our methods.