short-paper

Modelling Emotion Dynamics on Twitter via Hidden Markov Model

Authors:

Debashis Naskar,

Miguel Rebollo,

Subhashis DasAuthors Info & Claims

iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

Pages 245 - 249

https://doi.org/10.1145/3366030.3366092

Published: 22 February 2020 Publication History

Abstract

Exploring the mechanism about users' emotion dynamics towards social events and further predicting their future emotions have attracted great attention to the researchers. One of the unexplored components of human communication found online in written form is an emotional expression. However, despite the concreteness of the online expressions in written form, it remains unpredictable which kinds of emotions will be expressed in individual messages of Twitter users. To investigate this, we perform an investigation on observing emotions unfolding in a consecutive sequence of tweets for a particular user based on his/her past history. In this paper, we propose a method on given a set of tweets related with some events (identified by the usage of a hashtag), determines how those sentiments will be distributed on behalf of a person within a conversation. We present the Hidden Markov Model (HMM) to understand the nature of emotion dynamics in Twitter messages.

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Cited By

Hao FPark EChon K(2022)Social Media and Disaster Risk Reduction and Management: How Have Reddit Travel Communities Experienced the COVID-19 Pandemic?Journal of Hospitality & Tourism Research10.1177/1096348022108111548:1(58-83)Online publication date: 1-Mar-2022
https://doi.org/10.1177/10963480221081115
Luo RNettasinghe BKrishnamurthy V(2022)Controlling Segregation in Social Network Dynamics as an Edge Formation GameIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.31627899:4(2317-2329)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TNSE.2022.3162789
Li Z(2021)Forecast and Simulation of the Public Opinion on the Public Policy Based on the Markov ModelComplexity10.1155/2021/99369652021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/9936965

Index Terms

Modelling Emotion Dynamics on Twitter via Hidden Markov Model
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis

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iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

December 2019

709 pages

ISBN:9781450371797

DOI:10.1145/3366030

Copyright © 2019 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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JKU: Johannes Kepler Universität Linz
@WAS: International Organization of Information Integration and Web-based Applications and Services

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 February 2020

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iiWAS2019

iiWAS2019: The 21st International Conference on Information Integration and Web-based Applications & Services

December 2 - 4, 2019

Munich, Germany

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Cited By

Hao FPark EChon K(2022)Social Media and Disaster Risk Reduction and Management: How Have Reddit Travel Communities Experienced the COVID-19 Pandemic?Journal of Hospitality & Tourism Research10.1177/1096348022108111548:1(58-83)Online publication date: 1-Mar-2022
https://doi.org/10.1177/10963480221081115
Luo RNettasinghe BKrishnamurthy V(2022)Controlling Segregation in Social Network Dynamics as an Edge Formation GameIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.31627899:4(2317-2329)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TNSE.2022.3162789
Li Z(2021)Forecast and Simulation of the Public Opinion on the Public Policy Based on the Markov ModelComplexity10.1155/2021/99369652021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/9936965

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