Sentiment Analysis And Topic Extraction Related To The 2024 Indonesian Presidential And Vice Presisdential Election Using Deep Learning Methods - Tatulus, Raffael Lucas; Wulandhari, Lili Ayu (Universitas Dharma Wacana, 2024)
This research examines public sentiment and discourse surrounding the 2024 Indonesian Presidential and Vice Presidential Election through analysis of YouTube comments. Using a combination of deep learning techniques, specifically Long Short-Term Memory (LSTM) networks for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic extraction, we analyzed public responses to the three presidential candidates. The LSTM model achieved varying accuracy rates across candidates: 58% for Anies Baswedan, 61% for Prabowo Subianto, and 71% for Ganjar Pranowo, with consistently high recall rates of 100% across all candidates. Topic extraction through LDA revealed distinct themes in public discourse, including leadership qualities, policy implementations, and campaign promises. The research methodology involved web scraping YouTube comments from January to October 2023, followed by comprehensive text preprocessing and analysis. Our findings provide valuable insights into public opinion dynamics and key discussion topics during the election period, contributing to the understanding of social media's role in Indonesian political discourse. This study demonstrates the effectiveness of combining deep learning approaches for analyzing large-scale social media data in the context of political communications