Fine-grained Sentiment Analysis of Chinese Reviews Using LSTM Network

Customer reviews on online shopping platforms have potential commercial value. Realizing business intelligence by automatically extracting customers’ emotional attitude toward product features from a large amount of reviews, through fine-grained sentiment analysis, is of great importance. Long short-term memory (LSTM) network performs well in sentiment analysis of English reviews. A novel method that extended the network to Chinese product reviews was proposed to improve the performance of sentiment analysis on Chinese product reviews. Considering the differences between Chinese and English, a series of revisions were made to the Chinese corpus, such as word segmentation and stop word pruning. The review corpora vectorization was achieved by word2vec, and a LSTM network model was established based on the mathematical theories of the recurrent neural network. Finally, the feasibility of the LSTM model in finegrained sentiment analysis on Chinese product reviews was verified via experiment. Results demonstrate that the maximum accuracy of the experiment is 90.74%, whereas the maximum of F-score is 65.47%. The LSTM network proves to be feasible and effective when applied to sentiment analysis on product features of Chinese customer reviews. The performance of the LSTM network on fine-grained sentiment analysis is noticeably superior to that of the traditional machine learning method. This study provides references for fine-grained sentiment analysis on Chinese customer

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