Abstract
The emotional tendencies of product reviews on web have an important influence. Analysis of the sentiment of reviews on the Internet became very necessary. In this paper, a new sentiment analysis algorithm is utilized to analyze sentiment of Chinese product reviews. At training stage, a model based on skip-gram is proposed to train phrase vectors respectively on positive and negative reviews, which represent the semantic relationship of phrases. The predication of emotional tendencies of reviews based on the phrase vectors. The model does not need any modeling and feature extraction for the review data, thus it is applicable for massive data. Experimental results show that when dealing with massive data, the algorithm is better than traditional algorithms on both accuracy and learning time.
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Chen, H., Jin, H., Yuan, P., Zhu, L., Zhu, H. (2015). Sentiment Classification for Chinese Product Reviews Based on Semantic Relevance of Phrase. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_28
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DOI: https://doi.org/10.1007/978-3-319-25255-1_28
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