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Jointly Discovering Fine-grained and Coarse-grained Sentiments via Topic Modeling

Authors:

Hanqi Wang,

Fei Wu,

Xi Li,

Siliang Tang,

Jian Shao,

Yueting ZhuangAuthors Info & Claims

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

Pages 913 - 916

https://doi.org/10.1145/2647868.2655067

Published: 03 November 2014 Publication History

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Abstract

The ever-increasing user-generated contents in social media and other web services make it highly desirable to discover opinions of users on all kinds of topics. Motivated by the assumption that individual word and paragraph in documents will deliver fine-grained (e.g., "laudatory", "annoyed" or "boring") and coarse-grained (e.g., positive, negative or neutral) sentiments about certain topics respectively, this paper focuses on a deeper thematic level to jointly disentangle fine-grained and coarse-grained opinions towards topics in terms of sentiment analysis, named as LDA with multi-grained sentiments (MgS-LDA). As a result, the proposed MgS-LDA not only discovers the topics in social media, but also identifies opinions about a given topic in terms of fine-grained and coarse-grained sentiment. Results of several experiments show that our proposed MgS-LDA achieves better performance on both sentimental classification and topic modeling than related methods.

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Cited By

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Chen DZhengwei HJintao MKhanal R(2024)Sentiments analysis for intelligent customer service dialogue using hybrid word embedding and stacking ensembleSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-024-09899-228:19(11619-11631)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00500-024-09899-2
Pathak APandey MRautaray S(2021)Topic-level sentiment analysis of social media data using deep learningApplied Soft Computing10.1016/j.asoc.2021.107440108(107440)Online publication date: Sep-2021
https://doi.org/10.1016/j.asoc.2021.107440
Mahalakshmi GHemadharsana SMuthuselvi GSendhilkumar S(2020)Learning Deep Topics of InterestNew Trends in Computational Vision and Bio-inspired Computing10.1007/978-3-030-41862-5_156(1517-1532)Online publication date: 2020
https://doi.org/10.1007/978-3-030-41862-5_156
Show More Cited By

Index Terms

Jointly Discovering Fine-grained and Coarse-grained Sentiments via Topic Modeling
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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Information & Contributors

Information

Published In

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

November 2014

1310 pages

ISBN:9781450330633

DOI:10.1145/2647868

General Chairs:
Kien A. Hua
University of Central Florida, USA
,
Yong Rui
Microsoft Research, China
,
Ralf Steinmetz
Technische Universitt Darmstadt, Germany
,
Program Chairs:
Alan Hanjalic
Delft University of Technology, Netherlands
,
Apostol (Paul) Natsev
Google, USA
,
Wenwu Zhu
Tsinghua University, China

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2014

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Funding Sources

the Fundamental Research Funds for the Central Universities
Ministry of Science and Technology of the People's Republic of China
Program for New Century Excellent Talents in University
Zhejiang University of Science and Technology
National Natural Science Foundation of China
Natural Science Foundation of Zhejiang Province

Conference

MM '14

Sponsor:

SIGMM

MM '14: 2014 ACM Multimedia Conference

November 3 - 7, 2014

Florida, Orlando, USA

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MM '14 Paper Acceptance Rate 55 of 286 submissions, 19%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all

Chen DZhengwei HJintao MKhanal R(2024)Sentiments analysis for intelligent customer service dialogue using hybrid word embedding and stacking ensembleSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-024-09899-228:19(11619-11631)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00500-024-09899-2
Pathak APandey MRautaray S(2021)Topic-level sentiment analysis of social media data using deep learningApplied Soft Computing10.1016/j.asoc.2021.107440108(107440)Online publication date: Sep-2021
https://doi.org/10.1016/j.asoc.2021.107440
Mahalakshmi GHemadharsana SMuthuselvi GSendhilkumar S(2020)Learning Deep Topics of InterestNew Trends in Computational Vision and Bio-inspired Computing10.1007/978-3-030-41862-5_156(1517-1532)Online publication date: 2020
https://doi.org/10.1007/978-3-030-41862-5_156
Kordumova SGemert JSnoek C(2016)Exploring the Long Tail of Social Media TagsProceedings, Part I, of the 22nd International Conference on MultiMedia Modeling - Volume 951610.1007/978-3-319-27671-7_5(51-62)Online publication date: 4-Jan-2016
https://dl.acm.org/doi/10.1007/978-3-319-27671-7_5

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