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            <namePart type="given">Bharathi</namePart>
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    <abstract>Spreading positive vibes or hope content on social media may help many people to get motivated in their life. To address Hope Speech detection in YouTube comments, this paper presents the description of the models submitted by our team - MUCIC, to the Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) shared task at Association for Computational Linguistics (ACL) 2022. This shared task consists of texts in five languages, namely: English, Spanish (in Latin scripts), and Tamil, Malayalam, and Kannada (in code-mixed native and Roman scripts) with the aim of classifying the YouTube comment into “Hope”, “Not-Hope” or “Not-Intended” categories. The proposed methodology uses the re-sampling technique to deal with imbalanced data in the corpus and obtained 1st rank for English language with a macro-averaged F1-score of 0.550 and weighted-averaged F1-score of 0.860. The code to reproduce this work is available in GitHub.</abstract>
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%0 Conference Proceedings
%T MUCIC@LT-EDI-ACL2022: Hope Speech Detection using Data Re-Sampling and 1D Conv-LSTM
%A Gowda, Anusha
%A Balouchzahi, Fazlourrahman
%A Shashirekha, Hosahalli
%A Sidorov, Grigori
%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 gowda-etal-2022-mucic
%X Spreading positive vibes or hope content on social media may help many people to get motivated in their life. To address Hope Speech detection in YouTube comments, this paper presents the description of the models submitted by our team - MUCIC, to the Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) shared task at Association for Computational Linguistics (ACL) 2022. This shared task consists of texts in five languages, namely: English, Spanish (in Latin scripts), and Tamil, Malayalam, and Kannada (in code-mixed native and Roman scripts) with the aim of classifying the YouTube comment into “Hope”, “Not-Hope” or “Not-Intended” categories. The proposed methodology uses the re-sampling technique to deal with imbalanced data in the corpus and obtained 1st rank for English language with a macro-averaged F1-score of 0.550 and weighted-averaged F1-score of 0.860. The code to reproduce this work is available in GitHub.
%R 10.18653/v1/2022.ltedi-1.20
%U https://aclanthology.org/2022.ltedi-1.20
%U https://doi.org/10.18653/v1/2022.ltedi-1.20
%P 161-166

Markdown (Informal)

[MUCIC@LT-EDI-ACL2022: Hope Speech Detection using Data Re-Sampling and 1D Conv-LSTM](https://aclanthology.org/2022.ltedi-1.20) (Gowda et al., LTEDI 2022)

• MUCIC@LT-EDI-ACL2022: Hope Speech Detection using Data Re-Sampling and 1D Conv-LSTM (Gowda et al., LTEDI 2022)

ACL

• Anusha Gowda, Fazlourrahman Balouchzahi, Hosahalli Shashirekha, and Grigori Sidorov. 2022. MUCIC@LT-EDI-ACL2022: Hope Speech Detection using Data Re-Sampling and 1D Conv-LSTM. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 161–166, Dublin, Ireland. Association for Computational Linguistics.