Sentiment Analysis on Public Opinions Regarding the 2024 Regional Elections Using Long Short-Term Memory (LSTM), Random Forest, and Naive Bayes
Abstract
The 2024 Regional Elections (Pilkada) have become a hot topic, sparking extensive discussions on social media, particularly Twitter/X. This study aims to analyze public sentiment regarding the elections using three primary approaches: Long Short-Term Memory (LSTM), Random Forest, and Naive Bayes. The data undergoes six pre-processing stages and is represented using Word2Vec. The findings indicate that LSTM achieves the best performance with an accuracy of 94.21%, followed by Random Forest (92.78%) and Naive Bayes (89.43%). Further analysis reveals that negative sentiments are predominantly influenced by policies and candidates’ performance. This research provides insights into the effectiveness of deep learning methods compared to traditional techniques in social media sentiment analysis
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