Paper Info
Title
Mining Textual Reviews With Hierarchical Latent Tree Analysis
Abstract
Collecting feedback from customers is an important task of any business if they hope to retain customers and improve their quality of service. Nowadays, customers can enter reviews on many websites. The vast number of textual reviews make it difficult for customers or businesses to read directly. To analyze text data, topic modeling methods are usually used. In this paper, we propose to analyze textual reviews using a recently developed topic modeling method called hierarchical latent tree analysis, which has been shown to produce topic hierarchy better than some state-of-the-art topic modeling methods. We test the method using textual reviews written about restaurants on the Yelp website. We show that the topic hierarchy reveals useful insights about the reviews. We further show how to find interesting topics specific to locations.
Year
DOI
Venue
2017
10.1007/978-3-319-61845-6_40
DATA MINING AND BIG DATA, DMBD 2017
Keywords
Field
DocType
Review text mining, Hierarchical latent tree analysis, Topic modeling, Yelp Dataset Challenge, Latent tree models
Data science,Data mining,Computer science,Quality of service,Topic model,Hierarchy
Conference
Volume
ISSN
Citations 
10387
0302-9743
0
PageRank 
References 
Authors
0.34
6
3
Name
Order
Citations
PageRank
Leonard K. M. Poon19410.96
Chun Fai Leung200.34
Nevin .L Zhang389597.21