Analysis of Public Opinion on Public Transportation in Bandung and Jakarta in Twitter using Indonesian Bidirectional Encoder Representations from Transformer

Abstract

Transportation has been one of the main challenges for people living in urban areas, especially in big cities. Handling transportation problems traditionally is no longer considered suitable due to the increasingly large and complex data, which calls for an intelligent transportation system. One source of data that can be used to is social media (Twitter), in which the development of user-generated content can improve the management of existing transportation systems. In this study, IndoBERT, as a state-of-the-art model in natural language processing tasks, is used to perform sentiment analysis on Indonesian tweets about public transportation to have a better understanding of tweet context. Experimental results show that IndoBERT performs better than traditional machine learning algorithm, with the best combination of hyperparameter tuning results in accuracy of 94.8% which generalizes best to the dataset.