@inproceedings{thandavamurthi-etal-2023-tercet,
title = "{TERCET}@{LT}-{EDI}-2023: Hope Speech Detection for Equality, Diversity, and Inclusion",
author = "Thandavamurthi, Priyadharshini and
Sivakumar, Samyuktaa and
Sureshnathan, Shwetha and
D., Thenmozhi and
B, Bharathi and
Gl, Gayathri",
editor = "Chakravarthi, Bharathi R. and
Bharathi, B. and
Griffith, Joephine and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ltedi-1.39",
pages = "257--261",
abstract = "Hope is a cheerful and optimistic state of mind which has its basis in the expectation of positive outcomes. Hope speech reflects the same as they are positive words that can motivate and encourage a person to do better. Non-hope speech reflects the exact opposite. They are meant to ridicule or put down someone and affect the person negatively. The shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI - RANLP 2023 was created with data sets in English, Spanish, Bulgarian and Hindi. The purpose of this task is to classify human-generated comments on the platform, YouTube, as Hope speech or non-Hope speech. We employed multiple traditional models such as SVM (support vector machine), Random Forest classifier, Naive Bayes and Logistic Regression. Support Vector Machine gave the highest macro average F1 score of 0.49 for the training data set and a macro average F1 score of 0.50 for the test data set.",
}
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<abstract>Hope is a cheerful and optimistic state of mind which has its basis in the expectation of positive outcomes. Hope speech reflects the same as they are positive words that can motivate and encourage a person to do better. Non-hope speech reflects the exact opposite. They are meant to ridicule or put down someone and affect the person negatively. The shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI - RANLP 2023 was created with data sets in English, Spanish, Bulgarian and Hindi. The purpose of this task is to classify human-generated comments on the platform, YouTube, as Hope speech or non-Hope speech. We employed multiple traditional models such as SVM (support vector machine), Random Forest classifier, Naive Bayes and Logistic Regression. Support Vector Machine gave the highest macro average F1 score of 0.49 for the training data set and a macro average F1 score of 0.50 for the test data set.</abstract>
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%0 Conference Proceedings
%T TERCET@LT-EDI-2023: Hope Speech Detection for Equality, Diversity, and Inclusion
%A Thandavamurthi, Priyadharshini
%A Sivakumar, Samyuktaa
%A Sureshnathan, Shwetha
%A D., Thenmozhi
%A B, Bharathi
%A Gl, Gayathri
%Y Chakravarthi, Bharathi R.
%Y Bharathi, B.
%Y Griffith, Joephine
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F thandavamurthi-etal-2023-tercet
%X Hope is a cheerful and optimistic state of mind which has its basis in the expectation of positive outcomes. Hope speech reflects the same as they are positive words that can motivate and encourage a person to do better. Non-hope speech reflects the exact opposite. They are meant to ridicule or put down someone and affect the person negatively. The shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI - RANLP 2023 was created with data sets in English, Spanish, Bulgarian and Hindi. The purpose of this task is to classify human-generated comments on the platform, YouTube, as Hope speech or non-Hope speech. We employed multiple traditional models such as SVM (support vector machine), Random Forest classifier, Naive Bayes and Logistic Regression. Support Vector Machine gave the highest macro average F1 score of 0.49 for the training data set and a macro average F1 score of 0.50 for the test data set.
%U https://aclanthology.org/2023.ltedi-1.39
%P 257-261
Markdown (Informal)
[TERCET@LT-EDI-2023: Hope Speech Detection for Equality, Diversity, and Inclusion](https://aclanthology.org/2023.ltedi-1.39) (Thandavamurthi et al., LTEDI-WS 2023)
ACL
- Priyadharshini Thandavamurthi, Samyuktaa Sivakumar, Shwetha Sureshnathan, Thenmozhi D., Bharathi B, and Gayathri Gl. 2023. TERCET@LT-EDI-2023: Hope Speech Detection for Equality, Diversity, and Inclusion. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 257–261, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.