Twitter Sentiment Analysis Towards Candidates of the 2024 Indonesian Presidential Election

Indonesia will hold general elections in 2024. Long before the elections were held, the topic related to elections was widely discussed on news portals and social media, including Twitter. A few studies related to Indonesian election have tried to predict candidates who will run for the presidential election, but there has been no research that examines public sentiment on social media towards each of the potential candidates. The main objective of this study is to analyze the public sentiment in Twitter towards potential candidates for the 2024 Indonesian presidential election. This research seeks to fill the gaps in previous research and become a reference for further research regarding the sentiment analysis for election prediction using Twitter. The presidential candidates used in the research are the top 3 candidates based on the Poltracking survey, namely Ganjar Pranowo, Prabowo Subianto, and Anies Baswedan. The data were taken from January until October 2022, more than a year before the general election began. To predict the sentiment, four different machine-learning methods were used and compared to each other. There are Naïve Bayes, Support Vector Machine, Random Forest, and Neural Networks. The result shows that the number of tweets discussing each candidate from January until October 2022 has increased over time for each month. Based on the sentiment results of each candidate, the highest sentiment towards Prabowo is neutral (55.49%), the highest sentiment towards Ganjar is positive (61.34%), and the highest sentiment towards Anies is neutral (44.84%). Result from the study also shows that Anies was the presidential candidate who received more negative sentiment than the other two (56.63%). Meanwhile, Ganjar Pranowo got the most positive sentiment of all (42,69%). For the neutral sentiment, Anies Baswedan also got the most results (39,87%), followed by Prabowo (38.99%) and Ganjar Pranowo (21.14%). Result of the study also discovers that Random Forest and Neural Networks have the best performance for sentiment analysis. Other than that, experiment from this research also discovered that using a model for each entity can generate sentiment results specific to the candidate being analyzed, rather than sentiment for the tweet as a whole. This show that a model for each entity can give better results than using an aggregated model to determine the sentiment of each candidate.