摘要
电商市场日益完善,网络购物成为更多人的消费方式,用户在电商平台上保留了大量的产品评论信息,通过人工对文本评论情感分类任务愈加艰巨,文本情感的自动分类作为自然语言处理技术的重要一门,近年来受到各界的广泛关注。本文首先对京东网页上爬取的某商品评论文本做预处理,重点研究词袋模型和TF-IDF两种文本特征选择方法下不同文本分类算法的分类效果,研究结果表明TF-IDF下的文本分类效果显著优于词袋模型。
E-commerce market is becoming more and more perfect,online shopping has become more and more people's consump⁃tion mode,users have retained a large number of product comment information on the e-commerce platform,through manual text comment emotional classification task is becoming more and more arduous.As an important natural language processing technolo⁃gy,text emotion automatic classification has attracted wide attention in recent years.This paper first preprocesses the text of a com⁃modity comment crawling on the JingDong web page,focusing on the classification effect of different text classification algorithms under the word bag model and TF-IDF two text feature selection methods.The results show that the text classification effect under TF-IDF is significantly better than that of the word bag model.
作者
阎亚亚
YAN Ya-ya(Chongqing Industrial and Commercial University,Chongqing 400067,China)
出处
《电脑知识与技术》
2021年第28期138-140,共3页
Computer Knowledge and Technology
