Analysis of Hate Speech and Emotion Classification on Twitter Platform Using Long-Short Term Memory (LSTM) Method
Analisis Klasifikasi Hate Speech dan Emosi Pada Platfrom Twitter Menggunakan Metode Long-Short Term Memory (LSTM)
DOI:
https://doi.org/10.21070/ups.5287Keywords:
Hate Speech, LSTM, TwitterAbstract
This research aims to develop a classification model to detect hate speech and emotions on the Twitter platform used the 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. 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.
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