Customer Review Analysis Using Word Embedding Model Considering Text Topics
Customer Review Analysis Using Word Embedding Model Considering Text Topics
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发表时间：
2020
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通讯作者：
Mirai Igarashi;P. K. Kannan;Nobuhiko Terui
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作者：
Mirai Igarashi;P. K. Kannan;Nobuhiko Terui
顾客经常以顾客评论的形式反馈对产品和服务的评价和体验，发展顾客评论分析技术在现代营销研究中占有重要地位。现有的市场营销研究使用主题模型来捕捉评论生成行为，但这种方法忽略了词的顺序，即假设一个词袋，因此即使使用主题模型也不能充分考虑文本的上下文。在本研究中，我们提出了一个结合监督主题模型和词嵌入模型的模型，用于估计客户评论中提到的产品属性与其满意度之间的关系，同时捕获客户生成的评论文本的上下文。在实证分析中，我们将所提出的模型应用于某化妆品电商网站上关于睫毛膏相关产品的真实客户评论数据，结果表明，我们的模型捕获了一些与睫毛膏产品相关的可解释主题，并估计了它们对满意度得分的影响，例如评论中提到的“睫毛”主题往往导致高满意度，而“刷子”主题往往导致低满意度。
Customers often give feedback on their evaluations and experiences with the products and service in the form of customer reviews, and developing the technology of customer review analysis plays an important role in the modern marketing research. Existing studies on marketing have used topic models to capture the review generating behaviors, but this approach ignores the word ordering, that is, it assumes a bag-of-words, and thus cannot adequately consider the context of text even with topic models. In this study, we propose a model combining supervised topic model and word embedding model for estimating the relationship between the product attributes mentioned in the customer review and their satisfactions while capturing the context of review text customers generate. In the empirical analysis, we apply the proposed model to a real customer review data on mascara-related products on a cosmetics e-commerce site, and the results show that our model captures some interpretable topics related to mascara products and estimates their effects on satisfaction scores, for example, the “eyelash” topic mentioned in the review tends to result in high levels of satisfaction, while the “brush” topic is associated with low levels of satisfaction.