research-article

Parametric and Non-parametric User-aware Sentiment Topic Models

Authors:

Alexander Kotov,

Shiyong LuAuthors Info & Claims

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 413 - 422

https://doi.org/10.1145/2766462.2767758

Published: 09 August 2015 Publication History

Abstract

The popularity of Web 2.0 has resulted in a large number of publicly available online consumer reviews created by a demographically diverse user base. Information about the authors of these reviews, such as age, gender and location, provided by many on-line consumer review platforms may allow companies to better understand the preferences of different market segments and improve their product design, manufacturing processes and marketing campaigns accordingly. However, previous work in sentiment analysis has largely ignored these additional user meta-data. To address this deficiency, in this paper, we propose parametric and non-parametric User-aware Sentiment Topic Models (USTM) that incorporate demographic information of review authors into topic modeling process in order to discover associations between market segments, topical aspects and sentiments. Qualitative examination of the topics discovered using USTM framework in the two datasets collected from popular online consumer review platforms as well as quantitative evaluation of the methods utilizing those topics for the tasks of review sentiment classification and user attribute prediction both indicate the utility of accounting for demographic information of review authors in opinion mining.

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Cited By

Tutubalina ENikolenko S(2023)Topic Models with Sentiment Priors Based on Distributed RepresentationsJournal of Mathematical Sciences10.1007/s10958-023-06525-8273:4(639-652)Online publication date: 24-Jun-2023
https://doi.org/10.1007/s10958-023-06525-8
Arkhangelskaya ENikolenko S(2023)Deep Learning for Natural Language Processing: A SurveyJournal of Mathematical Sciences10.1007/s10958-023-06519-6273:4(533-582)Online publication date: 26-Jun-2023
https://doi.org/10.1007/s10958-023-06519-6
Bonifazi GCauteruccio FCorradini EMarchetti MSciarretta LUrsino DVirgili L(2022)A Space-Time Framework for Sentiment Scope Analysis in Social MediaBig Data and Cognitive Computing10.3390/bdcc60401306:4(130)Online publication date: 3-Nov-2022
https://doi.org/10.3390/bdcc6040130
Show More Cited By

Index Terms

Parametric and Non-parametric User-aware Sentiment Topic Models
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2015

1198 pages

ISBN:9781450336215

DOI:10.1145/2766462

General Chair:
Ricardo Baeza-Yates
Yahoo Labs, USA
,
Program Chairs:
Mounia Lalmas
Yahoo Labs, UK
,
Alistair Moffat
University of Melbourne, Australia
,
Berthier Ribeiro-Neto
Google, Brazil, and UFMG, Brazil

Copyright © 2015 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 the author(s) 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: 09 August 2015

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SIGIR '15

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SIGIR

SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval

August 9 - 13, 2015

Santiago, Chile

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SIGIR '15 Paper Acceptance Rate 70 of 351 submissions, 20%;

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

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Cited By

Tutubalina ENikolenko S(2023)Topic Models with Sentiment Priors Based on Distributed RepresentationsJournal of Mathematical Sciences10.1007/s10958-023-06525-8273:4(639-652)Online publication date: 24-Jun-2023
https://doi.org/10.1007/s10958-023-06525-8
Arkhangelskaya ENikolenko S(2023)Deep Learning for Natural Language Processing: A SurveyJournal of Mathematical Sciences10.1007/s10958-023-06519-6273:4(533-582)Online publication date: 26-Jun-2023
https://doi.org/10.1007/s10958-023-06519-6
Bonifazi GCauteruccio FCorradini EMarchetti MSciarretta LUrsino DVirgili L(2022)A Space-Time Framework for Sentiment Scope Analysis in Social MediaBig Data and Cognitive Computing10.3390/bdcc60401306:4(130)Online publication date: 3-Nov-2022
https://doi.org/10.3390/bdcc6040130
Osmani AMohasefi J(2022)Opinion Mining Using Enriched Joint Sentiment-Topic ModelInternational Journal of Information Technology & Decision Making10.1142/S021962202250058422:01(313-375)Online publication date: 28-Sep-2022
https://doi.org/10.1142/S0219622022500584
Komwad NTiwari PPraveen BChowdary C(2022)A survey on review summarization and sentiment classificationKnowledge and Information Systems10.1007/s10115-022-01728-y64:9(2289-2327)Online publication date: 1-Aug-2022
https://doi.org/10.1007/s10115-022-01728-y
Sun YHayashi TOhsawa Y(2021)A Latent Topic Analysis and Visualization Framework for Category-Level Target Promotion in the SupermarketThe Review of Socionetwork Strategies10.1007/s12626-021-00092-7Online publication date: 27-Sep-2021
https://doi.org/10.1007/s12626-021-00092-7
Osmani AMohasefi JGharehchopogh F(2020)Enriched Latent Dirichlet Allocation for Sentiment AnalysisExpert Systems10.1111/exsy.1252737:4Online publication date: 28-Jan-2020
https://doi.org/10.1111/exsy.12527
Kunaefi AAritsugi M(2020)Characterizing User Decision based on Argumentative Reviews2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)10.1109/BDCAT50828.2020.00002(161-170)Online publication date: Dec-2020
https://doi.org/10.1109/BDCAT50828.2020.00002
Huang HShen HMeng ZChang HHe H(2019)Community-based influence maximization for viral marketingApplied Intelligence10.1007/s10489-018-1387-849:6(2137-2150)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10489-018-1387-8
Mostafa L(2019)Student Sentiment Analysis Using Gamification for Education ContextProceedings of the International Conference on Advanced Intelligent Systems and Informatics 201910.1007/978-3-030-31129-2_30(329-339)Online publication date: 2-Oct-2019
https://doi.org/10.1007/978-3-030-31129-2_30
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