摘要
文章以对电脑产品为实验对象,通过网络爬虫对评论数据进行爬取,并将用户评论进行分词处理,而后就处理结果分别基于TF-IDF和Word2vec两者进行文本分析,计算该评论中的高频词语及其相关性,从而了解用户对该类产品的关注点及与之相关的其他问题,最后为生产商及电商平台提出指导性建议。
The paper takes computer products as the experimental object. Crawlers are used to get the comment data, and user comments are processed by word segmentation, and then text analysis is carried out based on TF-IDF and Word2vec respectively to calculate the high-frequency words in the comments and their correlation, so as to understand the users’ concerns about this kind of product and other related problems, and finally put forward guiding suggestions for manufacturers and e-commerce platforms.
作者
刘宇韬
施莉
刘诗含
LIUYutao;SHI Li;LIU Shihan(School of Logistics,Chengdu University of Information Technology,Chengdu 610103,China)
出处
《成都航空职业技术学院学报》
2022年第4期89-92,共4页
Journal of Chengdu Aeronautic Polytechnic
基金
四川省成都市科技局软科学项目“物流枢纽引领成都‘五链融合’创新发展的运行机制”(2021-RK00-00229-ZF)
四川省教育厅2022年省级大学生创新创业训练计划项目“电商网站数据挖掘与分析研究”(202210621365)。
