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Clustered Model Adaption for Personalized Sentiment Analysis

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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cover image ACM Other conferences
WWW '17: Proceedings of the 26th International Conference on World Wide Web
April 2017
1678 pages
ISBN:9781450349130

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  • IW3C2: International World Wide Web Conference Committee

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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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Author Tags

  1. model adaptation
  2. multi-task learning
  3. sentiment analysis

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  • Research-article

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WWW '17
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  • IW3C2

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WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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  • (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
  • (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
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  • (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
  • (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
  • (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
  • (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
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