Cluster analysis based on opinion mining

Mining based on opinions can extract useful information from users' comments. After doing cluster and analysis on the information, users can get a detailed understanding of the commodity, then determine to buy the commodity or not. In this paper, firstly, we extract evaluation objects and evaluation words, then cluster the evaluation objects. Next based on SO-PMI algorithm, judge the polarity of evaluation words and determine their polarity intensity values, then use K-means clustering algorithm to cluster the evaluation words. Last, for every kind of target evaluation object, make a count on the proportion of each kind evaluation word, and show the result to users in an intuitive way. This paper uses noun phrase pattern to match comments to extract evaluation objects and put forward the thematic words extraction algorithm. On judging the evaluation words' polarity, this paper establishes an emotional seed dictionary for each target object. The method of establishing dictionary for every attribute can eliminate the influence that less-correlation evaluation words have on the polarity judgment.