Sentiment Analysis of the 2024 General Election Through Twitter using Long-Short-Term Memory Algorithm

Abstract

This study analyses sentiment related to the 2024 Indonesian Presidential Election using the Long Short-Term Memory (LSTM) algorithm. A total of 2,400 tweets in the Indonesian language were gathered, with approximately 400 tweets sampled per week. In the data preparation, lexicon-based sentiment tagging, oversampling for class balance, and the creation and training of an LSTM model are all included in the study approach. The built model consists of embedding layers, Conv1D, and two LSTM layers. The LSTM model was selected due to its ability to capture long-range contextual dependencies in sequential text data like tweets, facilitated by its gate mechanisms (input, forget, output) that regulate information flow. The model achieved 84.3% accuracy in classifying sentiments (positive, neutral, negative), demonstrating its potential for real-time public opinion monitoring. The results provide actionable insights for election organisers and political analysts. For further study, using a wider spectrum of data to supplement model performance will help development. Tweaking hyperparameters and playing with other architectural models like GRUs or Transformers could improve model accuracy. Improved sentiment tagging calls for a more thorough and relevant sentiment vocabulary. The proposed model can be further developed into a real-time sentiment analysis tool to provide insights into public opinion on elections and other concerns.