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. Copyright © 2017 Springer International Publishing AG.
|Title of host publication||Data mining and big data: Second International Conference, DMBD 2017, Fukuoka, Japan, July 27-August 1, 2017, Proceedings|
|Editors||Ying TAN , Hideyuki TAKAGI , Yuhui SHI|
|Place of Publication||Cham|
|Publication status||Published - 2017|
CitationPoon, L. K., Leung, C. F., & Zhang, N. L. (2017). Mining textual reviews with hierarchical latent tree analysis. In Y. Tan, H. Takagi, & Y. Shi (Eds.), Data mining and big data: Second International Conference, DMBD 2017, Fukuoka, Japan, July 27-August 1, 2017, Proceedings (pp. 401-408). Cham: Springer International Publishing.
- Review text mining
- Hierarchical latent tree analysis
- Topic modeling
- Yelp Dataset Challenge
- Latent tree models