Improving Social Emotion Prediction with Reader Comments Integration

Abdullah Alsaedi, Phillip Brooker, Floriana Grasso, Stuart Thomason

2022

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.

Paper Citation

in Harvard Style

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, pages 285-292. DOI: 10.5220/0010837000003116

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Alsaedi A.
AU - Brooker P.
AU - Grasso F.
AU - Thomason S.
PY - 2022
SP - 285
EP - 292
DO - 10.5220/0010837000003116