research-article

Latent aspect rating analysis on review text data: a rating regression approach

Authors:

Chengxiang ZhaiAuthors Info & Claims

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 783 - 792

https://doi.org/10.1145/1835804.1835903

Published: 25 July 2010 Publication History

Abstract

In this paper, we define and study a new opinionated text data analysis problem called Latent Aspect Rating Analysis (LARA), which aims at analyzing opinions expressed about an entity in an online review at the level of topical aspects to discover each individual reviewer's latent opinion on each aspect as well as the relative emphasis on different aspects when forming the overall judgment of the entity. We propose a novel probabilistic rating regression model to solve this new text mining problem in a general way. Empirical experiments on a hotel review data set show that the proposed latent rating regression model can effectively solve the problem of LARA, and that the detailed analysis of opinions at the level of topical aspects enabled by the proposed model can support a wide range of application tasks, such as aspect opinion summarization, entity ranking based on aspect ratings, and analysis of reviewers rating behavior.

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Index Terms

Latent aspect rating analysis on review text data: a rating regression approach
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

July 2010

1240 pages

ISBN:9781450300551

DOI:10.1145/1835804

General Chairs:
Bharat Rao
Siemens
,
Balaji Krishnapuram
Siemens
,
Program Chairs:
Andrew Tomkins
Google Inc.
,
Qiang Yang
Hong Kong University of Science and Technology

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 July 2010

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KDD '10: The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 25 - 28, 2010

DC, Washington, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.3390/digital4030034
Mendez Guzman ESchlegel VBatista-Navarro R(2024)From outputs to insights: a survey of rationalization approaches for explainable text classificationFrontiers in Artificial Intelligence10.3389/frai.2024.13635317Online publication date: 23-Jul-2024
https://doi.org/10.3389/frai.2024.1363531
Perea-Khalifi DIrimia-Diéguez APalos-Sánchez P(2024)Exploring the determinants of the user experience in P2P payment systems in Spain: a text mining approachFinancial Innovation10.1186/s40854-023-00496-010:1Online publication date: 2-Jan-2024
https://doi.org/10.1186/s40854-023-00496-0
Ariza-Casabona ABoratto LSalamó M(2024)A Comparative Analysis of Text-Based Explainable Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688069(105-115)Online publication date: 8-Oct-2024
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