SSN_ARMM@ LT-EDI -ACL2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model
Praveenkumar Vijayakumar | Prathyush S | Aravind P | Angel S | Rajalakshmi Sivanaiah | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
In recent years social media has become one of the major forums for expressing human views and emotions. With the help of smartphones and high-speed internet, anyone can express their views on Social media. However, this can also lead to the spread of hatred and violence in society. Therefore it is necessary to build a method to find and support helpful social media content. In this paper, we studied Natural Language Processing approach for detecting Hope speech in a given sentence. The task was to classify the sentences into ‘Hope speech’ and ‘Non-hope speech’. The dataset was provided by LT-EDI organizers with text from Youtube comments. Based on the task description, we developed a system using the pre-trained language model BERT to complete this task. Our model achieved 1st rank in the Kannada language with a weighted average F1 score of 0.750, 2nd rank in the Malayalam language with a weighted average F1 score of 0.740, 3rd rank in the Tamil language with a weighted average F1 score of 0.390 and 6th rank in the English language with a weighted average F1 score of 0.880.
- Praveenkumar Vijayakumar 1
- Aravind P 1
- Angel S 1
- Rajalakshmi Sivanaiah 1
- Sakaya Milton Rajendram 1
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