research-article

Coupling topic modelling in opinion mining for social media analysis

Authors:

Md Mostafijur Rahman,

Ji ZhangAuthors Info & Claims

WI '17: Proceedings of the International Conference on Web Intelligence

Pages 533 - 540

https://doi.org/10.1145/3106426.3106459

Published: 23 August 2017 Publication History

Abstract

Many of social media platforms such as Facebook and Twitter make it easy for everyone to share their thoughts on literally anything. Topic and opinion detection in social media facilitates the identification of emerging societal trends, analysis of public reactions to policies and business products. In this paper, we proposed a new method that combines the opining mining and context-based topic modelling to analyse public opinions on social media data. Context based topic modelling is used to categorise data in groups and discover hidden communities in data group. The unwanted data group discovered by the topic model then will be discarded. A lexicon based opinion mining method will be applied to the remaining data groups to spot out the public sentiment about the entities. A set of Tweets data on Australian Federal Election 2010 was used in our experiments. Our experimental results demonstrate that, with the help of topic modelling, our social media analysis model is accurate and effective.

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Cited By

Paulus NLautenschlaeger LSchaefers W(2024)Social Media and Real Estate: Do Twitter Users Predict REIT Performance?Journal of Real Estate Research10.1080/08965803.2024.2340915(1-34)Online publication date: 16-Apr-2024
https://doi.org/10.1080/08965803.2024.2340915
Karadağ BAkbulut AZaim A(2023)Cyberbullying detection through deep learning: A case study of Turkish celebrities on TwitterWeb Intelligence10.3233/WEB-22180521:1(61-70)Online publication date: 5-Apr-2023
https://doi.org/10.3233/WEB-221805
Bandi VTao XShaik TYong JZhang J(2023)The Impact on Employability by COVID-19 Pandemic - AI Case StudiesWeb Information Systems Engineering – WISE 202310.1007/978-981-99-7254-8_66(850-864)Online publication date: 21-Oct-2023
https://doi.org/10.1007/978-981-99-7254-8_66
Show More Cited By

Index Terms

Coupling topic modelling in opinion mining for social media analysis
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools

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cover image ACM Conferences

WI '17: Proceedings of the International Conference on Web Intelligence

August 2017

1284 pages

ISBN:9781450349512

DOI:10.1145/3106426

Conference Chair:
Amit Sheth
Wright State University GuoROLE@GENERAL CHAIR
,
General Chairs:
Axel Ngonga
Leipzig University, Germany
,
yin Wang
Chongqing University of Posts and Telecommunications, China
,
Elizabeth Chang
The University of New South Wales, Australia
,
Dominik Ślęzak
Infobright Inc. & University of Warsaw, Poland
,
Bogdan Franczyk
Leipzig University, Germany
,
Program Chairs:
Rainer Alt
Leipzig University, Germany
,
Xiaohui Tao
University of Southern Queensland, Australia

Copyright © 2017 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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SIGAI: ACM Special Interest Group on Artificial Intelligence
TCII: IEEE Computer Society Technical Committee on Intelligent Informatics
Web Intelligence Consortium

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2017

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WI '17: International Conference on Web Intelligence 2017

August 23 - 26, 2017

Leipzig, Germany

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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;

Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

Paulus NLautenschlaeger LSchaefers W(2024)Social Media and Real Estate: Do Twitter Users Predict REIT Performance?Journal of Real Estate Research10.1080/08965803.2024.2340915(1-34)Online publication date: 16-Apr-2024
https://doi.org/10.1080/08965803.2024.2340915
Karadağ BAkbulut AZaim A(2023)Cyberbullying detection through deep learning: A case study of Turkish celebrities on TwitterWeb Intelligence10.3233/WEB-22180521:1(61-70)Online publication date: 5-Apr-2023
https://doi.org/10.3233/WEB-221805
Bandi VTao XShaik TYong JZhang J(2023)The Impact on Employability by COVID-19 Pandemic - AI Case StudiesWeb Information Systems Engineering – WISE 202310.1007/978-981-99-7254-8_66(850-864)Online publication date: 21-Oct-2023
https://doi.org/10.1007/978-981-99-7254-8_66
Wee CZhou XSun RGururajan RTao XLi YWee N(2022)Triaging Medical Referrals Based on Clinical Prioritisation Criteria Using Machine Learning TechniquesInternational Journal of Environmental Research and Public Health10.3390/ijerph1912738419:12(7384)Online publication date: 16-Jun-2022
https://doi.org/10.3390/ijerph19127384
Gupta IChatterjee IGupta N(2022)Latent Semantic Analysis based Real-world Application of Topic Modeling: A Review Study2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS)10.1109/ICAIS53314.2022.9742848(1142-1149)Online publication date: 23-Feb-2022
https://doi.org/10.1109/ICAIS53314.2022.9742848
Tao XShannon-Honson ADelaney PLi LDann CLi YXie H(2022)Data Analytics on Online Student Engagement Data for Academic Performance ModelingIEEE Access10.1109/ACCESS.2022.320895310(103176-103186)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3208953
Wang JZhou YCooke F(2022)Low-carbon economy and policy implications: a systematic review and bibliometric analysisEnvironmental Science and Pollution Research10.1007/s11356-022-20381-0Online publication date: 29-Apr-2022
https://doi.org/10.1007/s11356-022-20381-0
Cortis KDavis B(2021)Over a decade of social opinion mining: a systematic reviewArtificial Intelligence Review10.1007/s10462-021-10030-2Online publication date: 25-Jun-2021
https://doi.org/10.1007/s10462-021-10030-2
Pimpalkar AJeberson Retna Raj R(2021)Social Network Opinion Mining and Sentiment Analysis: Classification Approaches, Trends, Applications and IssuesCongress on Intelligent Systems10.1007/978-981-33-6981-8_60(755-773)Online publication date: 28-May-2021
https://doi.org/10.1007/978-981-33-6981-8_60
Usmani UHaron NJaafar J(2021)A Natural Language Processing Approach to Mine Online Reviews Using Topic ModellingComputing Science, Communication and Security10.1007/978-3-030-76776-1_6(82-98)Online publication date: 20-May-2021
https://doi.org/10.1007/978-3-030-76776-1_6
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