[Objective] This study aims to build a sentiment analysis dictionary for the Chinese book reviews. [Methods] We first divided the user’s sentiments into seven categories, which were used to create the Chinese book review emotional word list. Then, chose seed terms from that list with the help of a basic sentiment analysis lexicon. Finally, used the improved SO-PMI algorithm and synonym expansion method to classify target terms from the real book reviews. [Results] With the help of this new book review sentiment analysis dictionary, the average precision, recall and F1 rates were 0.90, 0.83 and 0.85 respectively. [Limitations] The test corpus is relatively small, which might influence our results. [Conclusions] The proposed method was an effective and reliable way to conduct sentiment analysis for the Chinese book reviews.
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Guo Shunli,Zhang Xiangxian. Building Sentiment Analysis Dictionary for Chinese Book Reviews. New Technology of Library and Information Service, 2016, 32(2): 67-74.
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