| Summary: | 碩士 === 國立臺灣師範大學 === 資訊工程學系 === 106 === With the rapid development of the Internet, huge amount of information is an inevitable trend, which also includes lots of user comments. Many reviews do not include useful information, so extracting helpful comments from a large number of user reviews is the research goal of this paper.
There is no standard definition of review helpfulness, and as long as if it helps users to think about it, it can be helpful. Therefore, this study attempts to give comments by the characteristics of scores , as a basis for judgment.
This thesis takes the short stories of Yahoo movie as the research target.The study uses the CKIP (Chinese Knowledge Information Processing) to process the comments first, and then find out the TFIDF keywords, parts of speech and lengths of comments from the data. The TFIDF keyword are expanded to synonyms and antonyms by the online dictionary of Ministry of Education. NTUSD (National Taiwan University Semantic Dictionary) was used built by National Taiwan University to find out the sentiment words contained in each comment and to calculate the number of sentiment words. Using SVM training model and prediction results, the accuracy of 79.7% was obtained.
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