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A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution

Liang, Huizhi, Ganeshbabu, Umarani and Thorne, Tom (2020) A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution IEEE Xplore, 8.

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Abstract

Sentiment analysis is one of the key tasks of natural language understanding. Sentiment Evolution models the dynamics of sentiment orientation over time. It can help people have a more profound and deep understanding of opinion and sentiment implied in user generated content. Existing work mainly focuses on sentiment classi�cation, while the analysis of how the sentiment orientation of a topic has been in�uenced by other topics or the dynamic interaction of topics from the aspect of sentiment has been ignored. In this paper, we propose to construct a Gaussian Process Dynamic Bayesian Network to model the dynamics and interactions of the sentiment of topics on social media such as Twitter. We use Dynamic Bayesian Networks to model time series of the sentiment of related topics and learn relationships between them. The network model itself applies Gaussian Process Regression to model the sentiment at a given time point based on related topics at previous time.We conducted experiments on a real world dataset that was crawled from Twitter with 9.72 million tweets. The experiment demonstrates a case study of analysing the sentiment dynamics of topics related to the event Brexit.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
NameEmailORCID
Liang, Huizhi
Ganeshbabu, Umarani
Thorne, Tomtom.thorne@surrey.ac.uk
Date : 6 March 2020
DOI : 10.1109/ACCESS.2020.2979012
Copyright Disclaimer : This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Uncontrolled Keywords : Sentiment analysis, sentiment evolution, dynamic Bayesian network, social media, temporal dynamics.
Depositing User : James Marshall
Date Deposited : 16 Jun 2020 15:50
Last Modified : 16 Jun 2020 15:50
URI: http://epubs.surrey.ac.uk/id/eprint/857996

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