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        <title>IIITSurat@LT-EDI-ACL2022: Hope Speech Detection using Machine Learning</title>
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    <abstract>This paper addresses the issue of Hope Speech detection using machine learning techniques. Designing a robust model that helps in predicting the target class with higher accuracy is a challenging task in machine learning, especially when the distribution of the class labels is highly imbalanced. This study uses and compares the experimental outcomes of the different oversampling techniques. Many models are implemented to classify the comments into Hope and Non-Hope speech, and it found that machine learning algorithms perform better than deep learning models. The English language dataset used in this research was developed by collecting YouTube comments and is part of the task “ACL-2022:Hope Speech Detection for Equality, Diversity, and Inclusion”. The proposed model achieved a weighted F1-score of 0.55 on the test dataset and secured the first rank among the participated teams.</abstract>
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%0 Conference Proceedings
%T IIITSurat@LT-EDI-ACL2022: Hope Speech Detection using Machine Learning
%A Roy, Pradeep
%A Bhawal, Snehaan
%A Kumar, Abhinav
%A Chakravarthi, Bharathi Raja
%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 roy-etal-2022-iiitsurat
%X This paper addresses the issue of Hope Speech detection using machine learning techniques. Designing a robust model that helps in predicting the target class with higher accuracy is a challenging task in machine learning, especially when the distribution of the class labels is highly imbalanced. This study uses and compares the experimental outcomes of the different oversampling techniques. Many models are implemented to classify the comments into Hope and Non-Hope speech, and it found that machine learning algorithms perform better than deep learning models. The English language dataset used in this research was developed by collecting YouTube comments and is part of the task “ACL-2022:Hope Speech Detection for Equality, Diversity, and Inclusion”. The proposed model achieved a weighted F1-score of 0.55 on the test dataset and secured the first rank among the participated teams.
%R 10.18653/v1/2022.ltedi-1.13
%U https://aclanthology.org/2022.ltedi-1.13
%U https://doi.org/10.18653/v1/2022.ltedi-1.13
%P 120-126

Markdown (Informal)

[IIITSurat@LT-EDI-ACL2022: Hope Speech Detection using Machine Learning](https://aclanthology.org/2022.ltedi-1.13) (Roy et al., LTEDI 2022)

• IIITSurat@LT-EDI-ACL2022: Hope Speech Detection using Machine Learning (Roy et al., LTEDI 2022)

ACL

• Pradeep Roy, Snehaan Bhawal, Abhinav Kumar, and Bharathi Raja Chakravarthi. 2022. IIITSurat@LT-EDI-ACL2022: Hope Speech Detection using Machine Learning. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 120–126, Dublin, Ireland. Association for Computational Linguistics.