E-commerce product review sentiment classification based on a naïve Bayes continuous learning framework
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摘要
Although statistical learning methods have achieved success in e-commerce platform product review sentiment classification, two problems have limited its practical application: 1) The computational efficiency to process large-scale reviews; 2) the ability to continuously learn from increasing reviews and multiple domains. This paper presents a continuous naïve Bayes learning framework for large-scale and multi-domain e-commerce platform product review sentiment classification. While keeping the high computational efficiency of the traditional naïve Bayes model, we extend the parameter estimation mechanism in naïve Bayes to a continuous learning style. We furthermore propose ways to fine-tune the learned distribution based on three kinds of assumptions to better adapt to different domains. Experimental results on the Amazon product and movie review sentiment datasets show that our model can use the knowledge learned from past domains to guide learning in new domains, and has a better capacity of dealing with reviews that are continuously updated and come from different domains.
论文关键词:E-commerce,Sentiment classification,Opinion mining,Naïve Bayes,Continuous learning,Domain adaptation
论文评审过程:Received 1 August 2019, Revised 4 February 2020, Accepted 5 February 2020, Available online 13 February 2020, Version of Record 19 June 2020.
论文官网地址:https://doi.org/10.1016/j.ipm.2020.102221