Linguistic Feature-based Classification for Anger and Anticipation using Machine Learning

Kalaimagal Ramakrishnan, Vimala Balakrishnan, Kumanan Govaichelvan

2022

Abstract

Growing number of online discourses enables the development of emotion mining models using natural language processing techniques. However, language diversity and cultural disparity alters the sentiment orientation of words depending on the community and context. Therefore, this study investigates the impacts of linguistic features, namely lexical and syntactic, in predicting the presence two emotions among Malaysian YouTube users, anger and anticipation. Term Frequency–Inverse Document Frequency (TF-IDF), Unigrams, Bigrams and Parts-of-Speech Tags were used as features to observe the classification performance. The dataset used in this study contains 2500 YouTube comments by Malaysian users on 46 Covid-19 related videos. Comments were extracted from three prominent Malaysian-centric English news channels: Channel News Asia (CNA), The Star News, and New Strait Times, ranging from 16 March 2020 – 30 April 2020 (i.e., first lockdown phase). Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Multinomial Naïve Bayes were the six classification algorithms tested, with results indicating Support Vector Machine with TF-IDF provided the best performance, achieving accuracy of 76% and 73% for anger and anticipation, respectively.

Paper Citation

in Harvard Style

Ramakrishnan K., Balakrishnan V. and Govaichelvan K. (2022). Linguistic Feature-based Classification for Anger and Anticipation using Machine Learning. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-584-5, pages 140-147. DOI: 10.5220/0011289300003277

in Bibtex Style

@conference{delta22,
author={Kalaimagal Ramakrishnan and Vimala Balakrishnan and Kumanan Govaichelvan},
title={Linguistic Feature-based Classification for Anger and Anticipation using Machine Learning},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2022},
pages={140-147},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011289300003277},
isbn={978-989-758-584-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Linguistic Feature-based Classification for Anger and Anticipation using Machine Learning
SN - 978-989-758-584-5
AU - Ramakrishnan K.
AU - Balakrishnan V.
AU - Govaichelvan K.
PY - 2022
SP - 140
EP - 147
DO - 10.5220/0011289300003277