关键词: 隐式评价对象, 特征词, 聚类, [k-means]聚类算法

Abstract: Network review is not clearly pointed out that the review of opinion targets, it did not explicitly points out that the opinion is the price of the goods such as comments “things are a little expensive”. For this kind of comment, an extracting method of commodities implicit opinion targets should be proposed on text data sets. According to the sentence structure of comment short text, the paper construct is the candidate opinion target model and the feature words in the candidate opinion target is extended by using HowNet2000 conceptual dictionary to alleviate the problem in the lack of information in candidate opinion target. In order to get some implicit opinion objects, it clusters the candidate opinion targets by using the similarity between the feature words in the candidate opinion targets based on clustering algorithm of [k-means]. Finally, to measure the ability of the feature words in the candidate opinion targets indicate to implicit opinion targets by using[χ2]statistic so as to extract the implicit opinion targets in the comments. The experiments show that this method improves the accuracy of extracting implicit opinion targets.

Key words: implicit opinion targets, feature term, clustering, clustering algorithm of [k-means]