short-paper

Modeling Sentiment Evolution for Social Incidents

Authors:

Yunjie Wang,

Hui Li,

Chen LinAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 2413 - 2416

https://doi.org/10.1145/3357384.3358136

Published: 03 November 2019 Publication History

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Abstract

Modeling sentiment evolution for social incidents in Microblogs is of vital importance for both enterprises and government officials. Existing works on sentiment tracking are not satisfying, due to the lack of entity-level sentiment extraction and accurate sentiment shift detection. Identifying entity-level sentiment is challenging as Microbloggers often use multiple opinion expressions in a sentence which targets different entities. Moreover, the evolution of the background sentiment, which is essential to shift detection, is ignored in the previous study. To address these issues, we leverage the proximity information to obtain more precise entity-level sentiment extraction. Based on it, we propose to simultaneously model the evolution of background opinion and the sentiment shift using a state space model on the time series of sentiment polarities. Experiments on real data sets demonstrate that our proposed approaches outperform state-of-the-art methods on the task of modeling sentiment evolution for social incidents.

References

[1]

Anne S. Y. Cheung. The battle of microblogging for legal justice in china. University of Hong Kong Faculty of Law Research Paper, 2014. URL https://ssrn.com/abstract=2397060.

Crossref

Google Scholar

[2]

Anastasia Giachanou, Ida Mele, and Fabio Crestani. Explaining sentiment spikes in twitter. In CIKM, pages 2263--2268, 2016.

Digital Library

Google Scholar

[3]

Mikalai Tsytsarau, Themis Palpanas, and Malú Castellanos. Dynamics of news events and social media reaction. In KDD, pages 901--910, 2014.

Digital Library

Google Scholar

[4]

Yuanhua Lv and ChengXiang Zhai. Positional language models for information retrieval. In SIGIR, pages 299--306, 2009.

Digital Library

Google Scholar

[5]

Martin J. Wainwright and Michael I. Jordan. Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1 (1--2): 1--305, 2008.

Digital Library

Google Scholar

[6]

Arkaitz Zubiaga. A longitudinal assessment of the persistence of twitter datasets. JASIST, 69 (8): 974--984, 2018.

Google Scholar

[7]

Mike Thelwall, Kevan Buckley, Georgios Paltoglou, Di Cai, and Arvid Kappas. Sentiment in short strength detection informal text. JASIST, 61 (12): 2544--2558, 2010.

Crossref

Google Scholar

[8]

Md Rakibul Islam and Minhaz F. Zibran. Leveraging automated sentiment analysis in software engineering. In MSR, pages 203--214, 2017.

Digital Library

Google Scholar

[9]

Toufique Ahmed, Amiangshu Bosu, Anindya Iqbal, and Shahram Rahimi. Senticr: a customized sentiment analysis tool for code review interactions. In ASE, pages 106--111, 2017.

Crossref

Google Scholar

[10]

Hyunjun Ju and Hwanjo Yu. Sentiment classification with convolutional neural network using multiple word representations. In IMCOM, pages 9:1--9:7, 2018.

Digital Library

Google Scholar

[11]

Siwei Lai, Liheng Xu, Kang Liu, and Jun Zhao. Recurrent convolutional neural networks for text classification. In AAAI, pages 2267--2273, 2015.

Digital Library

Google Scholar

[12]

Johan Bollen, Huina Mao, and Alberto Pepe. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In ICWSM, pages 450--453, 2011.

Google Scholar

[13]

Shulong Tan, Yang Li, Huan Sun, Ziyu Guan, Xifeng Yan, Jiajun Bu, Chun Chen, and Xiaofei He. Interpreting the public sentiment variations on twitter. IEEE Trans. Knowl. Data Eng., 26 (5): 1158--1170, 2014.

Digital Library

Google Scholar

Cited By

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Zha WYe QLi JOzbay K(2023)A social media Data-Driven analysis for transport policy response to the COVID-19 pandemic outbreak in Wuhan, ChinaTransportation Research Part A: Policy and Practice10.1016/j.tra.2023.103669172(103669)Online publication date: Jun-2023
https://doi.org/10.1016/j.tra.2023.103669
Cui XFang W(2022)Aspect-based Sentiment Classification with Dual Cooperative Graph Attention NetworksProceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing10.1145/3578741.3578769(135-141)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3578741.3578769
Zha YXie YHuang QZeng JMa Y(2021)Aspect level sentiment classification with multi-scale information2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT)10.1109/CECIT53797.2021.00056(279-285)Online publication date: Dec-2021
https://doi.org/10.1109/CECIT53797.2021.00056
Show More Cited By

Index Terms

Modeling Sentiment Evolution for Social Incidents
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks

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Information

Published In

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, 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 2019

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National Natural Science Foundation of China

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CIKM '19

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CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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The 34th ACM International Conference on Information and Knowledge Management

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Cited By

View all

Zha WYe QLi JOzbay K(2023)A social media Data-Driven analysis for transport policy response to the COVID-19 pandemic outbreak in Wuhan, ChinaTransportation Research Part A: Policy and Practice10.1016/j.tra.2023.103669172(103669)Online publication date: Jun-2023
https://doi.org/10.1016/j.tra.2023.103669
Cui XFang W(2022)Aspect-based Sentiment Classification with Dual Cooperative Graph Attention NetworksProceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing10.1145/3578741.3578769(135-141)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3578741.3578769
Zha YXie YHuang QZeng JMa Y(2021)Aspect level sentiment classification with multi-scale information2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT)10.1109/CECIT53797.2021.00056(279-285)Online publication date: Dec-2021
https://doi.org/10.1109/CECIT53797.2021.00056
Huang FYuan CBi YLu J(2020)Exploiting Long-Term Dependency for Topic Sentiment AnalysisIEEE Access10.1109/ACCESS.2020.30399638(221963-221974)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3039963

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