Mining textual reviews with hierarchical latent tree analysis

Leonard K.M. Poon; Chun Fai Leung; Nevin L. Zhang

doi:10.1007/978-3-319-61845-6_40

Mining textual reviews with hierarchical latent tree analysis

Leonard K.M. Poon^*, Chun Fai Leung, Nevin L. Zhang

^*Corresponding author for this work

Department of Computer Science and Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Data Mining and Big Data - 2nd International Conference, DMBD 2017, Proceedings
Editors	Hideyuki Takagi, Yuhui Shi, Ying Tan
Publisher	Springer Verlag
Pages	401-408
Number of pages	8
ISBN (Print)	9783319618449
DOIs	https://doi.org/10.1007/978-3-319-61845-6_40
Publication status	Published - 2017
Event	2nd International Conference on Data Mining and Big Data, DMBD 2017 - Fukuoka, Japan Duration: 27 Jul 2017 → 1 Aug 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10387 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on Data Mining and Big Data, DMBD 2017
Country/Territory	Japan
City	Fukuoka
Period	27/07/17 → 1/08/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Hierarchical latent tree analysis
Latent tree models
Review text mining
Topic modeling
Yelp Dataset Challenge

Access to Document

10.1007/978-3-319-61845-6_40

Cite this

Poon, L. K. M., Leung, C. F., & Zhang, N. L. (2017). Mining textual reviews with hierarchical latent tree analysis. In H. Takagi, Y. Shi, & Y. Tan (Eds.), Data Mining and Big Data - 2nd International Conference, DMBD 2017, Proceedings (pp. 401-408). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10387 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61845-6_40

Poon, Leonard K.M. ; Leung, Chun Fai ; Zhang, Nevin L. / Mining textual reviews with hierarchical latent tree analysis. Data Mining and Big Data - 2nd International Conference, DMBD 2017, Proceedings. editor / Hideyuki Takagi ; Yuhui Shi ; Ying Tan. Springer Verlag, 2017. pp. 401-408 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{9dd000ea7029485ea162e13fd59e0930,

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.",

keywords = "Hierarchical latent tree analysis, Latent tree models, Review text mining, Topic modeling, Yelp Dataset Challenge",

author = "Poon, \{Leonard K.M.\} and Leung, \{Chun Fai\} and Zhang, \{Nevin L.\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 2nd International Conference on Data Mining and Big Data, DMBD 2017 ; Conference date: 27-07-2017 Through 01-08-2017",

year = "2017",

doi = "10.1007/978-3-319-61845-6\_40",

language = "English",

isbn = "9783319618449",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "401--408",

editor = "Hideyuki Takagi and Yuhui Shi and Ying Tan",

booktitle = "Data Mining and Big Data - 2nd International Conference, DMBD 2017, Proceedings",

}

Poon, LKM, Leung, CF & Zhang, NL 2017, Mining textual reviews with hierarchical latent tree analysis. in H Takagi, Y Shi & Y Tan (eds), Data Mining and Big Data - 2nd International Conference, DMBD 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10387 LNCS, Springer Verlag, pp. 401-408, 2nd International Conference on Data Mining and Big Data, DMBD 2017, Fukuoka, Japan, 27/07/17. https://doi.org/10.1007/978-3-319-61845-6_40

Mining textual reviews with hierarchical latent tree analysis. / Poon, Leonard K.M.; Leung, Chun Fai; Zhang, Nevin L.
Data Mining and Big Data - 2nd International Conference, DMBD 2017, Proceedings. ed. / Hideyuki Takagi; Yuhui Shi; Ying Tan. Springer Verlag, 2017. p. 401-408 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10387 LNCS).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Mining textual reviews with hierarchical latent tree analysis

AU - Poon, Leonard K.M.

AU - Leung, Chun Fai

AU - Zhang, Nevin L.

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Hierarchical latent tree analysis

KW - Latent tree models

KW - Review text mining

KW - Topic modeling

KW - Yelp Dataset Challenge

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000440465200040

UR - https://openalex.org/W2696239205

UR - https://www.scopus.com/pages/publications/85026752558

U2 - 10.1007/978-3-319-61845-6_40

DO - 10.1007/978-3-319-61845-6_40

M3 - Conference Paper published in a book

SN - 9783319618449

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 401

EP - 408

BT - Data Mining and Big Data - 2nd International Conference, DMBD 2017, Proceedings

A2 - Takagi, Hideyuki

A2 - Shi, Yuhui

A2 - Tan, Ying

PB - Springer Verlag

T2 - 2nd International Conference on Data Mining and Big Data, DMBD 2017

Y2 - 27 July 2017 through 1 August 2017

ER -

Poon LKM, Leung CF, Zhang NL. Mining textual reviews with hierarchical latent tree analysis. In Takagi H, Shi Y, Tan Y, editors, Data Mining and Big Data - 2nd International Conference, DMBD 2017, Proceedings. Springer Verlag. 2017. p. 401-408. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-61845-6_40

Mining textual reviews with hierarchical latent tree analysis

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this