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投稿时间:2017-12-21 修订日期:2018-01-11
投稿时间:2017-12-21 修订日期:2018-01-11
中文摘要: 首先,根据餐饮业网络评论文本对消费者情感极性进行预测,建立了Lasso-Logistic和Lasso-PCA两个预测模型.相比之下,Lasso-PCA模型整合了更多的变量信息,对文本的情感极性具有更好的预测效果;但是Lasso-PCA模型对变量的解释能力较弱,尤其在解释变量维度较高的情况下,Lasso-PCA模型很难分析出解释变量对被解释变量的影响.其次,对Lasso-Logistic模型的变量选择结果进一步分析发现,特色菜、服务态度和环境以及“美中不足”之处是影响消费者情感极性的显著因素.
中文关键词: 网络评论文本 情感极性 Lasso算法 Logistic回归 稀疏主成分回归
Abstract:First, to predict consumer sentiment polarity based on Chinese online review of catering industry, this study establishes Lasso-Logistic and Lasso-PCA models. By comparison, Lasso-PCA model is more accurate by integrating more information of variables. However, Lasso-PCA model has weaker explanatory power especially in the scenario of high dimensional data. Second, using the variable selection results of Lasso-Logistic model, we find that specialties, service attitude, and the external environment, as well as "a fly in the ointment" are the significant factors affecting the consumer's emotional polarity directly.
keywords: text mining sentiment polarity Lasso algorithm Logistic regression sparse principal component regression
文章编号: 中图分类号: 文献标志码:
基金项目:
作者 | 单位 | |
杨博文 | 南京财经大学 经济学院 统计系, 南京 210023 | yangbowen0717@163.com |
Author Name | Affiliation | |
YANG Bo-Wen | Department of Statistics, School of Economics, Nanjing University of Finances and Economics, Nanjing 210023, China | yangbowen0717@163.com |
引用文本:
杨博文.基于餐饮业网络评论的消费者情感极性分析.计算机系统应用,2018,27(8):42-48
YANG Bo-Wen.Analysis of Consumer Sentiment Polarity Based on Chinese Online Review of Catering Industry.COMPUTER SYSTEMS APPLICATIONS,2018,27(8):42-48
杨博文.基于餐饮业网络评论的消费者情感极性分析.计算机系统应用,2018,27(8):42-48
YANG Bo-Wen.Analysis of Consumer Sentiment Polarity Based on Chinese Online Review of Catering Industry.COMPUTER SYSTEMS APPLICATIONS,2018,27(8):42-48