short-paper

Leveraging Personalized Sentiment Lexicons for Sentiment Analysis

Authors:

Dominic Seyler,

ChengXiang ZhaiAuthors Info & Claims

ICTIR '20: Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval

Pages 109 - 112

https://doi.org/10.1145/3409256.3409850

Published: 14 September 2020 Publication History

Abstract

We propose a novel personalized approach for the sentiment analysis task. The approach is based on the intuition that the same sentiment words can carry different sentiment weights for different users. For each user, we learn a language model over a sentiment lexicon to capture her writing style. We further correlate this user-specific language model with the user's historical ratings of reviews. Additionally, we discuss how two standard CNN and CNN+LSTM models can be improved by adding these user-based features. Our evaluation on the Yelp dataset shows that the proposed new personalized sentiment analysis features are effective.

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Cited By

Sharma SRaj MAli MSharma KSingh ASingh S(2024)Design of Structure using an Adaptive Generative AI System for Customised Eduction for Individuals2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)10.1109/IC2PCT60090.2024.10486396(276-280)Online publication date: 9-Feb-2024
https://doi.org/10.1109/IC2PCT60090.2024.10486396
Zhu WQiu JYu ZLuo W(2024)A survey on personalized document-level sentiment analysisNeurocomputing10.1016/j.neucom.2024.128449(128449)Online publication date: Aug-2024
https://doi.org/10.1016/j.neucom.2024.128449
Qiu JYu ZLuo W(2023)UCM: Personalized Document-Level Sentiment Analysis Based on User Correlation MiningAdvanced Intelligent Computing Technology and Applications10.1007/978-981-99-4752-2_38(456-471)Online publication date: 31-Jul-2023
https://doi.org/10.1007/978-981-99-4752-2_38

Index Terms

Leveraging Personalized Sentiment Lexicons for Sentiment Analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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cover image ACM Conferences

ICTIR '20: Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval

September 2020

207 pages

ISBN:9781450380676

DOI:10.1145/3409256

General Chairs:
Krisztian Balog
University of Stavanger, Norway
,
Vinay Setty
University of Stavanger, Norway
,
Program Chairs:
Christina Lioma
University of Copenhagen, Denmark
,
Yiqun Liu
Tsinghua University, China
,
Min Zhang
Tsinghua University, China
,
Klaus Berberich
HTW Saar & MPI for Informatics, Germany

Copyright © 2020 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: 14 September 2020

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National Science Foundation

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ICTIR '20

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SIGIR

ICTIR '20: The 2020 ACM SIGIR International Conference on the Theory of Information Retrieval

September 14 - 17, 2020

Virtual Event, Norway

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Cited By

Sharma SRaj MAli MSharma KSingh ASingh S(2024)Design of Structure using an Adaptive Generative AI System for Customised Eduction for Individuals2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)10.1109/IC2PCT60090.2024.10486396(276-280)Online publication date: 9-Feb-2024
https://doi.org/10.1109/IC2PCT60090.2024.10486396
Zhu WQiu JYu ZLuo W(2024)A survey on personalized document-level sentiment analysisNeurocomputing10.1016/j.neucom.2024.128449(128449)Online publication date: Aug-2024
https://doi.org/10.1016/j.neucom.2024.128449
Qiu JYu ZLuo W(2023)UCM: Personalized Document-Level Sentiment Analysis Based on User Correlation MiningAdvanced Intelligent Computing Technology and Applications10.1007/978-981-99-4752-2_38(456-471)Online publication date: 31-Jul-2023
https://doi.org/10.1007/978-981-99-4752-2_38

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