Optimizing Twitter Sentiment Analysis on Tapera Policy Using SVM and PSO

This study aims to analyse the sentiment of Twitter users towards the Public Housing Savings (Tapera) policy in Indonesia using the Support Vector Machine (SVM) algorithm optimised by Particle Swarm Optimization (PSO). In recent years, social media has emerged as a primary platform for individuals to express their views and opinions on public policies. The government programme, Tapera, which was designed to increase access to housing for the public, attracted considerable attention, with a range of responses, including both positive and negative sentiments. The methodology employed in this study comprised the collection of data from Twitter, the processing of text, and the application of SVM-based classification techniques, reinforced by PSO, with the objective of enhancing the accuracy and efficiency of the model. The results demonstrated that the PSO-optimised SVM model exhibited an accuracy of 85%, accompanied by an Area Under Curve (AUC) value of 0.84 and a ROC curve that indicated the model's notable capacity for differentiating between positive and negative sentiments. These findings indicate the existence of certain sentiment patterns that can be utilised for the evaluation and improvement of Tapera policies. In conclusion, this research is expected to provide a comprehensive picture of the public response to the Tapera policy and present an analytical model that can be applied to evaluate other policies. Further research is recommended to expand data coverage and develop algorithms to achieve more accurate results.