A Study On Sentiment Orientation Analysis Of Online Hotel Comments On Chinese Web

With the deeper and wider applications of the Internet, more and more customers browse large number of online reviews in order to know other customers word-of-mouth of product and service to make an informed choice. At the same time, the network customer reviews as a feedback mechanism can help vendors and manufacturers improve their products and service, and then get competitive advantage. However, with the e-business arising, the number of reviews is growing rapidly and the content is more complicated, it is very difficult to retrieve useful knowledge from customers’ reviews, especially difficult to get people’s perspectives and attitudes from many characters, events, products in a short period. It needs technical methods to improve the accuracy and convenience of mining information. Review mining comes about to extract valuable information from customers’ reviews, and the purpose of orientation analysis of review mining is to determine the attitude of the entire text by text mining and analysising the viewpoints, opinions, emotions, likes and dislikes, etc., of the subjective information in the text. It has attracted many researchers’ attention. It mainly includes sentiment classification, mining products features and learning subjective language etc. In English reviews area, Researchers have made some successful results but few studies have been conducted to Chinese customer reviews on the Internet. As Chinese e-business has increased dramatically in cyber space, how to automatically retrieve useful knowledge from online Chinese reviews has become urgent.In this paper, our research object is hotel online reviews which is important for travel booking, it is very representative and customers usually rely on it, according to the actual situation, we firstly categorize the comments, forming different dimensions, and we propose sentiment orientation model and use open source frame of web crawler, ICTCLAS Chinese text segmentation system, feature extraction by descending method, choose the tool R so that to make a multi algorithms comparison with complex review emotion words as experimental subjects, the experiment results show that this method is better and more suitable for web text sentiment classification, which overcomes the high-dimensional sparse data problems of textual analysis and noises in the training set. In addition, the method proposed here is efficient and effective to deal with huge web text.