Hateful Sentiment Detection in Real-Time Tweets: An LSTM-Based Comparative Approach

It is undeniable that social media has improved our lives in many ways, like allowing interactions with others all over the world and network expansion for businesses. However, there are detrimental effects of such accessibility, including the rapid spread of hate through offensive messages typically directed toward gender, religion, race, and disability, which can cause psychological harm. To address this problem of social media, many researchers have recently proposed various algorithms powered by machine learning (ML) and deep learning for the detection of hate speech. This work proposes a hate speech detection model based on long-short term memory (LSTM), using term frequency inverse document frequency (TF-IDF) vectorization, and makes comparisons with support vector machine (SVM), Naïve Bayes (NB), logistic regression (LR), XGBoost (XGB), random forest (RF),
$K$
-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.