Improving Social Emotion Prediction with Reader Comments Integration

Alsaedi, A ORCID: 0000-0003-3732-2452, Brooker, P ORCID: 0000-0003-1189-4647, Grasso, F ORCID: 0000-0001-8419-6554 and Thomason, S ORCID: 0009-0006-5814-4447 (2022) Improving Social Emotion Prediction with Reader Comments Integration In: 14th International Conference on Agents and Artificial Intelligence, 2022-2-3 - 2022-2-5.

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Abstract

Social emotion prediction is concerned with the prediction of the reader’s emotion when exposed to a text. In this paper, we propose a comment integration method for social emotion prediction. The basic intuition is that enriching social media posts with related comments can enhance the models’ ability to capture the conversation context, and hence improve the performance of social emotion prediction. We developed three models that use the comment integration method with different approaches: word-based, topic-based, and deep learning-based. Results show that our proposed models outperform popular models in terms of accuracy and F1-score.

Item Type:	Conference Item (Unspecified)
Uncontrolled Keywords:	Social Emotion Prediction, Emotion Analysis
Divisions:	Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	15 Mar 2022 15:07
Last Modified:	28 Feb 2026 23:52
DOI:	10.5220/0010837000003116
Open Access URL:	https://www.scitepress.org/Link.aspx?doi=10.5220/0...
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3150843
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