Sentiment Analysis Using Text Mining Techniques On Social Media Using the Support Vector Machine Method Case Study Seagames 2023 Football Final

Muhammad Rifa'i (STMIK KAPUTAMA)
Relita Buaton (STMIK KAPUTAMA)
I Gusti Prahmana (STMIK KAPUTAMA)

Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Vol. 3 No. 1 (2023): October 2023

This thesis aims to analyze sentiment on text data from social media related to the 2023 SEA Games, especially in the final match of the soccer sport. The method used is the Text Mining Technique with the SVM (Support Vector Machine) algorithm to classify user sentiment as positive or negative regarding the match. Text data is retrieved from various social media platforms during and after the match. The results of the sentiment analysis are expected to provide insight into the public's view of the sporting event. This research can contribute to the understanding of public sentiment towards the 2023 SEA Games final football match through the analysis of text data from social media.