Abstract
Every day, Facebook, Twitter, Weibo and other social network sites and major e-commerce sites generate a large number of online reviews with emotions. The analysing people’s opinions from these reviews can assist a variety of decision-making processes in organisations, products, and administrations. Therefore, it is practically and theoretically important to study how to analyse online reviews with emotions. To help researchers study sentiment analysis, in this paper, we survey the machine learning based method for sentiment analysis of online reviews. These methods are main based on Support Vector Machine, Neural Networks, Naïve Bayes, Bayesian network, Maximum entropy, and some hybrid methods. In particular, we point out the main problems in the machine learning based methods for sentiment analysis and the problems to be solved in the future.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 61762016), and Guangxi Key Lab of Multi-Source Information Mining & Security (No. 19-A-01-01).
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Lin, P., Luo, X. (2020). A Survey of Sentiment Analysis Based on Machine Learning. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12430. Springer, Cham. https://doi.org/10.1007/978-3-030-60450-9_30
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