Improving Social Emotion Prediction with Reader Comments Integration

Abdullah Alsaedi; Phillip Brooker; Floriana Grasso; Stuart Thomason

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Paper

Improving Social Emotion Prediction with Reader Comments Integration

Topics: Data Mining; Deep Learning; Emotional Intelligence; Machine Learning; Natural Language Processing

In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, 285-292, 2022

Authors: Abdullah Alsaedi ¹ ; Phillip Brooker ² ; Floriana Grasso ¹ and Stuart Thomason ¹

Affiliations: ¹ Department of Computer Science, University of Liverpool, U.K. ; ² Department of Sociology, Social Policy and Criminology, University of Liverpool, U.K.

Keyword(s): Social Emotion Prediction, Emotion Analysis.

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.

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Paper citation in several formats:

Alsaedi, A.; Brooker, P.; Grasso, F. and Thomason, S. (2022). Improving Social Emotion Prediction with Reader Comments Integration. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 285-292. DOI: 10.5220/0010837000003116

@conference{icaart22,
author={Abdullah Alsaedi. and Phillip Brooker. and Floriana Grasso. and Stuart Thomason.},
title={Improving Social Emotion Prediction with Reader Comments Integration},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={285-292},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010837000003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Improving Social Emotion Prediction with Reader Comments Integration
SN - 978-989-758-547-0
IS - 2184-433X
AU - Alsaedi, A.
AU - Brooker, P.
AU - Grasso, F.
AU - Thomason, S.
PY - 2022
SP - 285
EP - 292
DO - 10.5220/0010837000003116
PB - SciTePress