Hate Speech and Emotion Detection on Twitter Using LSTM Model

Abstract

This research aims to develop a classification model to detect hate speech and emotions on the Twitter platform used the Long-Short Term Memory (LSTM) method. With the increasing volume of data on social media, especially Twitter, automatic identification of negative content is crucial for maintaining a healthy digital ecosystem. The dataset used in this study consists of tweets labeled for hate speech and various emotion categories. The preprocessing process is carried out to clean and prepare the data, including steps such as punctuation removal, tokenization, and text normalization. After preprocessing, the dataset is split into training and testing data with a ratio of 60:40 to ensure accurate model evaluation. The experimental results show that the LSTM model achieves an accuracy of 89% in hate speech classification and 71% in emotion classification. These results demonstrate the potential of the LSTM method in text analysis tasks and can serve as a basis for developing automatic detection systems on social media platforms.

Highlights:

• LSTM achieves 89% accuracy in detecting hate speech and 71% in emotion classification.
• The model processes Indonesian language tweets to identify hate speech and emotional tone.
• Preprocessing steps like tokenization and stopword removal are crucial for accurate classification.

Keywords: Hate Speech, LSTM, Twitter