$K$ -nearest neighbor (

$k$

-NN), artificial neural network (ANN), and bidirectional encoder representations from transformers (BERT) models. To validate and authenticate our proposed work, we obtained and classified a real-time Twitter data stream of a trending topic using Twitter API into two classes: hate speech and nonhate speech. The precision, recall, and

$F1$

score achieved by LSTM are 0.98, 0.99, and 0.98, respectively. The accuracy of LSTM for detecting hateful sentiment was found to be 97%, surpassing the accuracy of other models.">

Hateful Sentiment Detection in Real-Time Tweets: An LSTM-Based Comparative Approach (original) (raw)

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