Hate Comment Detection On Twitter Using Long Short Term Memory (LSTM) With Genetic Algorithm (GA)

Abstract

In the era of social media like today, one social media that is currently quite popular is Twitter. This study explores the use of the Long Short Term Memory (LSTM) method optimized with the Genetic Algorithm (GA) to detect hate speech in Twitter data in Indonesia. We use TF-IDF and GloVe feature extraction techniques to produce effective word vector representations in natural language processing. This study also introduces feature expansion and similarity corpus construction to improve the performance of the LSTM classification model. Evaluation is carried out through a confusion matrix to measure accuracy, precision, recall, and F1 score. The results show that the LSTM model with TF-IDF and GloVe feature extraction achieves the best performance with an accuracy of up to 92.91%. We also found that the combination of Unigram + Bigram + Trigram, max feature 10000, and Glove corpus with Top 20 similarity gave optimal results. In addition, parameter optimization using genetic algorithms has been shown to improve accuracy and F1-Score. The resulting LSTM model is able to classify test data with high accuracy, which has the potential to help in the detection and handling of hate speech on social media, as well as improving the model's ability to identify and understand text content in the Indonesian language context.