research-article

Modeling online reviews with multi-grain topic models

Authors:

Ryan McDonaldAuthors Info & Claims

WWW '08: Proceedings of the 17th international conference on World Wide Web

Pages 111 - 120

https://doi.org/10.1145/1367497.1367513

Published: 21 April 2008 Publication History

Abstract

In this paper we present a novel framework for extracting the ratable aspects of objects from online user reviews. Extracting such aspects is an important challenge in automatically mining product opinions from the web and in generating opinion-based summaries of user reviews [18, 19, 7, 12, 27, 36, 21]. Our models are based on extensions to standard topic modeling methods such as LDA and PLSA to induce multi-grain topics. We argue that multi-grain models are more appropriate for our task since standard models tend to produce topics that correspond to global properties of objects (e.g., the brand of a product type) rather than the aspects of an object that tend to be rated by a user. The models we present not only extract ratable aspects, but also cluster them into coherent topics, e.g., 'waitress' and 'bartender' are part of the same topic 'staff' for restaurants. This differentiates it from much of the previous work which extracts aspects through term frequency analysis with minimal clustering. We evaluate the multi-grain models both qualitatively and quantitatively to show that they improve significantly upon standard topic models.

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Cited By

Briciu ACălin AMiholca DMoroz-Dubenco CPetrașcu VDascălu G(2024)Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian ReviewsMathematics10.3390/math1203045612:3(456)Online publication date: 31-Jan-2024
https://doi.org/10.3390/math12030456
Suwanpipob WArch-Int NWunnasri W(2024)A Knowledge-Driven Approach for Automatic Semantic Aspect Term Extraction Using the Semantic Power of Linked Open DataApplied Sciences10.3390/app1413586614:13(5866)Online publication date: 4-Jul-2024
https://doi.org/10.3390/app14135866
Li RChen HFeng FMa ZWang XHovy E(2024)DualGCN: Exploring Syntactic and Semantic Information for Aspect-Based Sentiment AnalysisIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321961535:6(7642-7656)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3219615
Show More Cited By

Index Terms

Modeling online reviews with multi-grain topic models
1. Information systems
  1. Information retrieval
    1. Document representation
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

WWW '08: Proceedings of the 17th international conference on World Wide Web

April 2008

1326 pages

ISBN:9781605580852

DOI:10.1145/1367497

General Chairs:
Jinpeng Huai
Beihang University, China
,
Robin Chen
AT&T Labs, USA
,
Hsiao-Wuen Hon
Microsoft Research Asia, China
,
Yunhao Liu
HK University of Science and Technology, Hong Kong
,
Program Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Andrew Tomkins
Yahoo! Research, USA
,
Xiaodong Zhang
The Ohio State University, USA

Copyright © 2008 ACM.

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Published: 21 April 2008

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WWW '08: The 17th International World Wide Web Conference

April 21 - 25, 2008

Beijing, China

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Briciu ACălin AMiholca DMoroz-Dubenco CPetrașcu VDascălu G(2024)Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian ReviewsMathematics10.3390/math1203045612:3(456)Online publication date: 31-Jan-2024
https://doi.org/10.3390/math12030456
Suwanpipob WArch-Int NWunnasri W(2024)A Knowledge-Driven Approach for Automatic Semantic Aspect Term Extraction Using the Semantic Power of Linked Open DataApplied Sciences10.3390/app1413586614:13(5866)Online publication date: 4-Jul-2024
https://doi.org/10.3390/app14135866
Li RChen HFeng FMa ZWang XHovy E(2024)DualGCN: Exploring Syntactic and Semantic Information for Aspect-Based Sentiment AnalysisIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321961535:6(7642-7656)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3219615
Agathangelou PKatakis IKasnesis P(2024)Word- and Sentence-Level Representations for Implicit Aspect ExtractionIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339183311:5(5935-5948)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3391833
Meng FQi MLuo C(2024)Research Progress of Review Topic Mining Methods: From Word Frequency Statistics to Deep Learning2024 IEEE 13th International Conference on Communication Systems and Network Technologies (CSNT)10.1109/CSNT60213.2024.10545711(737-741)Online publication date: 6-Apr-2024
https://doi.org/10.1109/CSNT60213.2024.10545711
Zhang JDu ZSun SWang S(2024)Perception of customer satisfaction and complaints based on BERTopic and interpretable machine learning: evidence from hotels in Xi’anCurrent Issues in Tourism10.1080/13683500.2024.2389308(1-23)Online publication date: 13-Aug-2024
https://doi.org/10.1080/13683500.2024.2389308
Zhou ZJi FChang XLiu YFujita HWu J(2024)A novel self-supervised contrastive learning based sentence-level attribute induction method for online satisfaction evaluationComputers & Industrial Engineering10.1016/j.cie.2024.109981189(109981)Online publication date: Mar-2024
https://doi.org/10.1016/j.cie.2024.109981
Kang YYang XZhang LLuo XXu YWang HLiu J(2024)MGMFN: Multi-graph and MLP-mixer fusion network for Chinese social network sentiment classificationMultimedia Tools and Applications10.1007/s11042-023-17857-783:24(64989-65010)Online publication date: 18-Jan-2024
https://doi.org/10.1007/s11042-023-17857-7
Mor AOrsenigo CSoto Gomez MVercellis C(2024)Shaping the causes of product returns: topic modeling on online customer reviewsElectronic Commerce Research10.1007/s10660-024-09901-xOnline publication date: 22-Sep-2024
https://doi.org/10.1007/s10660-024-09901-x
Bashiri HNaderi H(2024)Comprehensive review and comparative analysis of transformer models in sentiment analysisKnowledge and Information Systems10.1007/s10115-024-02214-366:12(7305-7361)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10115-024-02214-3
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