Sentiment Analysis on Social Media: Investigating Users' Perceptions of MRT and LRT Transportation Services

Abstract

Providing excellent public transportation in response to the passenger’s complaints and recommendations results in long-term improvements to the service. This study investigates public perceptions of the MRT and LRT rail transportation services within the Klang Valley Integrated Transit System, operated by Rapid KL, through sentiment analysis in X. With 4.4 million users in Malaysia as of January 2022, X (previously Twitter) media social serves as a significant platform for public discourse. However, analyzing these perceptions poses challenges due to the limited platforms for analysis, and seeking from X is even more challenging due to the unstructured and noisy nature of the tweets. Therefore, this study aims to develop a sentiment analysis model that organizes tweets into structured data, utilizing machine learning techniques for sentiment classification into positive, neutral, and negative categories. Following the model implementation, the data are collected, translated, cleaned, labeled, analyzed, and classified using a Support Vector Machine before being deployed in a web system for ease of access. Analysis results revealed that user sentiment is predominantly neutral, with a significant focus on MRT services and topic finding related to scheduling. The model scored good accuracy 80% without a kernel and 84% with a Linear kernel, with evaluation metrics demonstrating strong performance on all three sentiment categories. Future enhancements will include label refining and applying more hyperparameter tuning to improve analysis accuracy.