Antomatic Sentiment Analysis for Web User Reviews
|Course||Applied Computer Technology|
|Keywords||Sentiment Analysis user review Sentiment classification Sentiment orientation Web mining|
With the increasement of the information on the Internet and the expansion of the network applications, there are more and more people obtain the information they needed by the Internet. Before users buy a product, does something, they often expect access some of the reviews and recommendations as a reference. And so, the Internet becomes a very important way. And there are many kinds of reviews and recommendations on the Internet, but it is a daunting task to discriminate them manually. So this paper prensents an approach for antomatic sentiment analysis.First, the paper will introduce Automatic acquisition of emotional Dictionary. Based on the algorithm in paper Using Tongyici Cilin to Compute Word Semantic Polarity proposed by Institute of Computer Science & Technology, we improve it according to extract some words those are wrong tagged and this makes the experimental results improve from 89.58% to 91.52%. Besides, we present a rule-based dynamic expansion method, determining the sentiment orientation of ambiguous words according to their contexts.Next the paper research one application of sentiment classification—sentiment analysis of user reviews. We use text vector model, according to the influences of characters of Chinese language, adversatives, privatives and degree adverbs, determining the sentiment orientation of the reviews. At the same time, expand the initial sentiment words by an iterative process. The method is simple, easy to understand, and the total accuracy achieves 86.43%, but has a high time complexity.In this paper, we mining the relatively reviews and recommendations, and classified according to user’s sentiment. Input the subject, and output the percentage of the three categories (Positive, Negative and Neutral). And for the highest category, give the top ten pieces of information which absolute values are highest.