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E-commerce product review sentiment classification based on a naïve Bayes continuous learning framework
Information Processing & Management ( IF 8.6 ) Pub Date : 2020-02-13 , DOI: 10.1016/j.ipm.2020.102221
Feng Xu , Zhenchun Pan , Rui Xia

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.



中文翻译:

基于朴素贝叶斯持续学习框架的电子商务产品评论情感分类

尽管统计学习方法在电子商务平台产品评论情感分类中取得了成功,但有两个问题限制了其实际应用:1)处理大规模评论的计算效率;2)从不断增加的评论和多个领域中不断学习的能力。本文提出了一个连续的朴素贝叶斯学习框架,用于大规模和多域电子商务平台产品评论情感分类。在保持传统朴素贝叶斯模型的高计算效率的同时,我们将朴素贝叶斯的参数估计机制扩展为连续学习风格。我们进一步提出了基于三种假设对学习分布进行微调的方法,以更好地适应不同领域。

更新日期:2020-04-21
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