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ReviewMiner: An Aspect-based Review Analytics System

Authors:

Hongning WangAuthors Info & Claims

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1285 - 1288

https://doi.org/10.1145/3077136.3084148

Published: 07 August 2017 Publication History

Abstract

We develop an aspect-based sentiment analysis system named ReviewMiner. It analyzes 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 his/her relative emphasis on different aspects when forming the overall judgment of the entity. The system personalizes the retrieved results according to users' input preferences over the identified aspects, recommends similar items based on the detailed aspect-level opinions, and summarizes aspect-level opinions in textual, temporal and spatial dimensions. The unique multi-modal opinion summarization and visualization mechanisms provide users with rich perspectives to digest information from user-generated opinionated content for making informed decisions.

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Cited By

Chauhan GNahta RMeena YGopalani D(2023)Aspect based sentiment analysis using deep learning approachesComputer Science Review10.1016/j.cosrev.2023.10057649:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.cosrev.2023.100576
Khan MJaved AIhsan MTariq U(2020)A novel category detection of social media reviews in the restaurant industryMultimedia Systems10.1007/s00530-020-00704-229:3(1825-1838)Online publication date: 24-Oct-2020
https://doi.org/10.1007/s00530-020-00704-2
Akiyama KMitsuzawa KKazuya NKumamoto TNadamoto A(2018)Clause-level Negative-opinion Analysis for Classifying Reviews on Multiple DomainsProceedings of the 20th International Conference on Information Integration and Web-based Applications & Services10.1145/3282373.3282405(113-121)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3282373.3282405

Index Terms

ReviewMiner: An Aspect-based Review Analytics System
1. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Online analytical processing
  2. World Wide Web
    1. Web searching and information discovery
      1. Content ranking
      2. Personalization

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Published In

cover image ACM Conferences

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2017

1476 pages

ISBN:9781450350228

DOI:10.1145/3077136

General Chairs:
Noriko Kando
National Institute of Informatics
,
Tetsuya Sakai
Waseda University
,
Hideo Joho
University of Tsukuba
,
Program Chairs:
Hang Li
Huawei Noah's Ark Lab
,
Arjen P. de Vries
Radboud University
,
Ryen W. White
Microsoft Cortana

Copyright © 2017 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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2017

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National Science Foundation

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SIGIR '17

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SIGIR

SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval

August 7 - 11, 2017

Tokyo, Shinjuku, Japan

Acceptance Rates

SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Chauhan GNahta RMeena YGopalani D(2023)Aspect based sentiment analysis using deep learning approachesComputer Science Review10.1016/j.cosrev.2023.10057649:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.cosrev.2023.100576
Khan MJaved AIhsan MTariq U(2020)A novel category detection of social media reviews in the restaurant industryMultimedia Systems10.1007/s00530-020-00704-229:3(1825-1838)Online publication date: 24-Oct-2020
https://doi.org/10.1007/s00530-020-00704-2
Akiyama KMitsuzawa KKazuya NKumamoto TNadamoto A(2018)Clause-level Negative-opinion Analysis for Classifying Reviews on Multiple DomainsProceedings of the 20th International Conference on Information Integration and Web-based Applications & Services10.1145/3282373.3282405(113-121)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3282373.3282405

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