Joint Classification Model of Topic and Polarity: Finding Satisfaction and Dissatisfaction Factors from Airport Service Review

被引：1

作者：

Mizufune, Kosuke ^{[1
]}

Katsumata, Sotaro ^{[1
]}

机构：

[1] Osaka Univ, Grad Sch Econ, 1-7 Machikaneyama, Toyonaka, Osaka 5600043, Japan

来源：

2018 18TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW) | 2018年

关键词：

D O I：

10.1109/ICDMW.2018.00126

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a model developed based on Latent Dirichlet Allocation (LDA). It incorporates both a document dataset and the polarity of the document, for example, a positive and negative evaluation, as input data. In the empirical analysis, it was applied to international airport user reviews, in which the quality of services is evaluated. The results show that the proposed model can classify reviews into topics as effectively as the original topic model, and that its user evaluation forecasting ability is also good. Furthermore, this study examined the automatic generation of a polarity dictionary by the model.

引用

页码：856 / 863

页数：8