Twitter Sentiment Analysis on the 2024 Presidential Dispute Decision Using Naïve Bayes and Svm
Year: 2024
Type: article
Abstract: Public sentiment regarding the 2024 presidential election dispute decision was analyzed through the Twitter platform. The method employed was Naïve Bayes, implemented using RapidMiner software. The dataset consisted of thousands of tweets collected during the presidential election dispute period. Each tweet was classified into three sentiment categories: positive, negative, and neutral. The text mining process involved data cleaning, tokenization, and the application of natural language processing (NLP) techniques for feature extraction. The results of the analysis revealed the distribution of sentiments among Twitter users and changes in sentiment trends over specific periods. This research is expected to provide insights into public perceptions and sentiment patterns related to the presidential election dispute decision
Source: Jurnal Riset Informatika
Authors: Ihsan Aulia Rahman, Nanang Ruhyana
Institution: Universitas Nusa Mandiri
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $32
OpenAlex ID: W4410043822