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E-Commerce Letters 2024
基于电商平台在线评论的运动相机消费者偏好趋势挖掘
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Abstract:
当前电商对于消费者在线评论关注度越来越高。其包含了消费者的使用体验、产品偏好等信息，能够帮助商家了解消费者满意度和未来偏好等，针对性地进行产品升级与营销调整，以更加迎合消费者购买倾向。本文结合文本挖掘、情感分析和Lasso-SVM筛选预测模型，挖掘消费者偏好趋势，为商家提供从在线评论文本提取隐含信息，有助于商家进一步了解未来产品优化方法。本文选取京东的运动相机进行实例分析，探索消费者偏好趋势挖掘，为在线评论的文本分析与偏好趋势挖掘提供了参考依据。
At present, e-commerce companies pay more and more attention to consumers’ online reviews. It contains consumers’ use experience, product preferences, and other information. For merchants, this implicit information can help them understand consumers’ satisfaction and future preferences, so as to carry out targeted product upgrades and marketing adjustments, so as to better cater to consumers’ purchase tendencies. In this paper, text mining, sentiment analysis and Lasso-SVM screening prediction model are combined to study and analyze online review text to predict consumer preference and provide a method for merchants to extract implied information from online review text and predict consumers’ future preference, which is helpful for merchants to further understand the method of future product optimization. This paper selected JD’s sports camera for example analysis to explore consumer preference trend mining and prediction, providing a reference for text analysis and preference trend mining of online reviews.
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