Emotion Classification for the 2024 General Election Using Lexicon Approach and Deep Learning Algorithm

The 2024 general election in Indonesia has been a major highlight in the development of democracy, triggering diverse emotional reactions amongst society. Various emotions such as anger, anticipation, disgust, fear, joy, sadness, surprise, and trust appeared on various social media platforms, especially X platform. This research uses datasets obtained using crawling techniques on the X platform. The method used is the NRC Lexicon approach to label emotions by mapping words that contain emotions in the text. Anger emotion is the most dominant emotion with a percentage reaching 33.36%. This research involves the use of deep learning models, namely Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), to classify emotions from the analyzed texts. In addition, this research also explores the application of the SMOTE technique to handle class imbalance in the data. With the application of SMOTE to the RNN model, the accuracy achieved was 74.47%, and in the LSTM model with the application of SMOTE, the accuracy reached 90.49%. The LSTM model (without SMOTE) achieved an accuracy value of 98.52%, precision 90.48% recall 87.48%, and f1-score 87.48%. While the RNN model (without SMOTE) achieved 92.52% accuracy, 90.48% precision, 85.75% recall, and 87.48% f1-score. The comparison with the RNN model confirms the superiority of LSTM in overcoming complexity and long-term patterns in structured text data.