Analysis of Public Sentiment Regarding the Issue of Cancelling the Revision of the 2024 Regional Election Law with NLP

Abstract

The latest changes regarding the age requirements for regional head candidates in the 2024 Election by the Supreme Court have given rise to various responses from the public. This decision has become an important issue that is widely discussed on social media and mass media, where this decision has caused various reactions, both positive, negative and neutral. Sentiment analysis is important to determine public opinion on this decision. This study aims to analyze public sentiment on the Issue of Cancellation of the Revision of the 2024 Pilkada Law. This study uses the Natural Language Processing (NLP) method with the Naive Bayes algorithm. The data used is text data in the form of public opinion on the Issue of Cancellation of the Revision of the 2024 Pilkada Law collected via Twitter. This data is then processed using NLP techniques such as cleaning, tokenization, normalization, filtering and stemming. After that, public opinion on the Issue of Cancellation of the Revision of the 2024 Pilkada Law is classified into positive, negative and neutral sentiment categories. The results of the study showed that the results of the model performance evaluation using data testing produced an accuracy of 92%, so it can be concluded that this model is good enough for sentiment analysis. Accuracy shows that the model makes correct predictions on 92% of the total data, which is dominated by the neutral class.