Aspect-Based Classification and Visualization of Twitter Sentiment Analysis Towards Online Food Delivery Services in Malaysia

Abstract

Twitter has become a popular platform for the citizens of Malaysia. Twitter’s ease of expressing opinions could be used to evaluate and review Malaysian Online Food Delivery (OFD) providers. Due to competition from other OFDs in Malaysia, companies need to know customer feedback. OFD reviews are unstructured and massive, making comparisons difficult. Next, some websites evaluate OFD yet only consider pricing, delivery time, and customer experience. Customers cannot visualize the comparison based on users’ preferences, bilingual reviews, and less time-consuming to visualize OFD using the website. Thus, this study aims to design a web application system that uses Naïve Bayes to categorize Twitter sentiment analysis (SA) on Malaysia’s best OFD. It is based on customer satisfaction, visualizing the results, developing the system, and evaluating its accuracy, functioning and usability. Users can read about specific OFD by viewing Twitter SA visualization or comparing them directly. Five aspect-based SA types were presented: affordable price, promotion and discount, review rating, delivery time and condition of food delivered. Functionality testing demonstrated the accomplishment of all objectives. The training and testing data could predict OFD’s Twitter sentiment with 71.67% and 76.29% accuracy for English and Bahasa Melayu, respectively. The system’s usability produced a 94.64% average score using System Usability Scale and was considered “excellent”. Thus, it can be concluded that this study can solve the mentioned issues of OFD and ease the aspect-based comparison.