Abstract
Hope endows the power required to conquer life’s challenges. Since the COVID-19 pandemic, hope speech detection has gained momentum in the natural language processing field due to the need for positive reinforcement on the internet. Hope speech detection identifies texts in social media comments that evoke positive feelings in people. This paper aims to identify hope speech in YouTube comments. To discern the hope speech as distinct from the YouTube comments, various machine learning and deep learning algorithms (Support Vector Machines, Logistic Regression, Convolutional Neural Networks + Bidirectional long-short term memory (Bi-LSTM), Multinomial Naive Bayes (MNB), Ensemble models) have been used. These YouTube comments have been created as a part of the task "EACL-2021: Hope Speech Detection for Equality, Diversity, and Inclusion". Based on the best configurations that showed the highest results in trials for the shared tasks, weighted F1-scores of 0.608 for Tamil and 0.911 for English were achieved.
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