@inproceedings{kumar-etal-2022-soa,
title = "{SOA}{\_}{NLP}@{LT}-{EDI}-{ACL}2022: An Ensemble Model for Hope Speech Detection from {Y}ou{T}ube Comments",
author = "Kumar, Abhinav and
Saumya, Sunil and
Roy, Pradeep",
editor = "Chakravarthi, Bharathi Raja and
Bharathi, B and
McCrae, John P and
Zarrouk, Manel and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ltedi-1.31",
doi = "10.18653/v1/2022.ltedi-1.31",
pages = "223--228",
abstract = "Language should be accommodating of equality and diversity as a fundamental aspect of communication. The language of internet users has a big impact on peer users all over the world. On virtual platforms such as Facebook, Twitter, and YouTube, people express their opinions in different languages. People respect others{'} accomplishments, pray for their well-being, and cheer them on when they fail. Such motivational remarks are hope speech remarks. Simultaneously, a group of users encourages discrimination against women, people of color, people with disabilities, and other minorities based on gender, race, sexual orientation, and other factors. To recognize hope speech from YouTube comments, the current study offers an ensemble approach that combines a support vector machine, logistic regression, and random forest classifiers. Extensive testing was carried out to discover the best features for the aforementioned classifiers. In the support vector machine and logistic regression classifiers, char-level TF-IDF features were used, whereas in the random forest classifier, word-level features were used. The proposed ensemble model performed significantly well among English, Spanish, Tamil, Malayalam, and Kannada YouTube comments.",
}
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<abstract>Language should be accommodating of equality and diversity as a fundamental aspect of communication. The language of internet users has a big impact on peer users all over the world. On virtual platforms such as Facebook, Twitter, and YouTube, people express their opinions in different languages. People respect others’ accomplishments, pray for their well-being, and cheer them on when they fail. Such motivational remarks are hope speech remarks. Simultaneously, a group of users encourages discrimination against women, people of color, people with disabilities, and other minorities based on gender, race, sexual orientation, and other factors. To recognize hope speech from YouTube comments, the current study offers an ensemble approach that combines a support vector machine, logistic regression, and random forest classifiers. Extensive testing was carried out to discover the best features for the aforementioned classifiers. In the support vector machine and logistic regression classifiers, char-level TF-IDF features were used, whereas in the random forest classifier, word-level features were used. The proposed ensemble model performed significantly well among English, Spanish, Tamil, Malayalam, and Kannada YouTube comments.</abstract>
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%0 Conference Proceedings
%T SOA_NLP@LT-EDI-ACL2022: An Ensemble Model for Hope Speech Detection from YouTube Comments
%A Kumar, Abhinav
%A Saumya, Sunil
%A Roy, Pradeep
%Y Chakravarthi, Bharathi Raja
%Y Bharathi, B.
%Y McCrae, John P.
%Y Zarrouk, Manel
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F kumar-etal-2022-soa
%X Language should be accommodating of equality and diversity as a fundamental aspect of communication. The language of internet users has a big impact on peer users all over the world. On virtual platforms such as Facebook, Twitter, and YouTube, people express their opinions in different languages. People respect others’ accomplishments, pray for their well-being, and cheer them on when they fail. Such motivational remarks are hope speech remarks. Simultaneously, a group of users encourages discrimination against women, people of color, people with disabilities, and other minorities based on gender, race, sexual orientation, and other factors. To recognize hope speech from YouTube comments, the current study offers an ensemble approach that combines a support vector machine, logistic regression, and random forest classifiers. Extensive testing was carried out to discover the best features for the aforementioned classifiers. In the support vector machine and logistic regression classifiers, char-level TF-IDF features were used, whereas in the random forest classifier, word-level features were used. The proposed ensemble model performed significantly well among English, Spanish, Tamil, Malayalam, and Kannada YouTube comments.
%R 10.18653/v1/2022.ltedi-1.31
%U https://aclanthology.org/2022.ltedi-1.31
%U https://doi.org/10.18653/v1/2022.ltedi-1.31
%P 223-228
Markdown (Informal)
[SOA_NLP@LT-EDI-ACL2022: An Ensemble Model for Hope Speech Detection from YouTube Comments](https://aclanthology.org/2022.ltedi-1.31) (Kumar et al., LTEDI 2022)
ACL