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Clustered Model Adaption for Personalized Sentiment Analysis

Authors:

Benjamin Haines,

Hongning WangAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 937 - 946

https://doi.org/10.1145/3038912.3052693

Published: 03 April 2017 Publication History

Abstract

We propose to capture humans' variable and idiosyncratic sentiment via building personalized sentiment classification models at a group level. Our solution roots in the social comparison theory that humans tend to form groups with others of similar minds and ability, and the cognitive consistency theory that mutual influence inside groups will eventually shape group norms and attitudes, with which group members will all shift to align. We formalize personalized sentiment classification as a multi-task learning problem. In particular, to exploit the clustering property of users' opinions, we impose a non-parametric Dirichlet Process prior over the personalized models, in which group members share the same customized sentiment model adapted from a global classifier. Extensive experimental evaluations on large collections of Amazon and Yelp reviews confirm the effectiveness of the proposed solution: it outperformed user-independent classification solutions, and several state-of-the-art model adaptation and multi-task learning algorithms.

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Cited By

Davani AAtari MKennedy BDehghani M(2023)Hate Speech Classifiers Learn Normative Social StereotypesTransactions of the Association for Computational Linguistics10.1162/tacl_a_0055011(300-319)Online publication date: 22-Mar-2023
https://doi.org/10.1162/tacl_a_00550
Kazienko PBielaniewicz JGruza MKanclerz KKaranowski KMiłkowski PKocoń J(2023)Human-centered neural reasoning for subjective content processingInformation Fusion10.1016/j.inffus.2023.01.01094:C(43-65)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.inffus.2023.01.010
Kocoń JBaran JKanclerz KKajstura MKazienko P(2023)Differential Dataset Cartography: Explainable Artificial Intelligence in Comparative Personalized Sentiment AnalysisComputational Science – ICCS 202310.1007/978-3-031-35995-8_11(148-162)Online publication date: 26-Jun-2023
https://doi.org/10.1007/978-3-031-35995-8_11
Show More Cited By

Index Terms

Clustered Model Adaption for Personalized Sentiment Analysis
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
      2. Sentiment analysis

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Published In

cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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National Science Foundation

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WWW '17

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IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

Acceptance Rates

WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

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

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Cited By

Davani AAtari MKennedy BDehghani M(2023)Hate Speech Classifiers Learn Normative Social StereotypesTransactions of the Association for Computational Linguistics10.1162/tacl_a_0055011(300-319)Online publication date: 22-Mar-2023
https://doi.org/10.1162/tacl_a_00550
Kazienko PBielaniewicz JGruza MKanclerz KKaranowski KMiłkowski PKocoń J(2023)Human-centered neural reasoning for subjective content processingInformation Fusion10.1016/j.inffus.2023.01.01094:C(43-65)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.inffus.2023.01.010
Kocoń JBaran JKanclerz KKajstura MKazienko P(2023)Differential Dataset Cartography: Explainable Artificial Intelligence in Comparative Personalized Sentiment AnalysisComputational Science – ICCS 202310.1007/978-3-031-35995-8_11(148-162)Online publication date: 26-Jun-2023
https://doi.org/10.1007/978-3-031-35995-8_11
Roy BDas S(2022)Perceptible sentiment analysis of students' WhatsApp group chats in valence, arousal, and dominance spaceSocial Network Analysis and Mining10.1007/s13278-022-01016-113:1Online publication date: 19-Dec-2022
https://doi.org/10.1007/s13278-022-01016-1
Zhao SXiao YGuo JYue XYang JKrishna RXu PKeutzer K(2021)Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple SourcesProceedings of the Web Conference 202110.1145/3442381.3449981(541-552)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3449981
Wang ZWang CLi LPan ZZou Y(2021)A Novel Text Personalized Analysis Model Based on BP Neural NetworkRecent Featured Applications of Artificial Intelligence Methods. LSMS 2020 and ICSEE 2020 Workshops10.1007/978-981-33-6378-6_32(430-441)Online publication date: 12-Jan-2021
https://doi.org/10.1007/978-981-33-6378-6_32
Zhang JLee Dd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)TOMATO: A Topic-Wise Multi-Task Sparsity ModelProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411972(1793-1802)Online publication date: 19-Oct-2020
https://doi.org/10.1145/3340531.3411972
Zou XYang JZhang WHan H(2020)Collaborative community-specific microblog sentiment analysis via multi-task learningExpert Systems with Applications10.1016/j.eswa.2020.114322(114322)Online publication date: Nov-2020
https://doi.org/10.1016/j.eswa.2020.114322
Gong LWang HGuo YFarooq F(2018)When Sentiment Analysis Meets Social NetworkProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3220120(1455-1464)Online publication date: 19-Jul-2018
https://dl.acm.org/doi/10.1145/3219819.3220120
Kim JHa YKang SLim HCha M(2018)Detecting Multiclass Emotions from Labeled Movie Scripts2018 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp.2018.00102(590-594)Online publication date: Jan-2018
https://doi.org/10.1109/BigComp.2018.00102
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