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

Authors

  • Khyrina Airin Fariza Abu Samah College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Melaka Branch, Jasin Campus, Melaka, Malaysia
  • Nur Shahirah Jailani College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Melaka Branch, Jasin Campus, Melaka, Malaysia
  • Raseeda Hamzah College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Melaka Branch, Jasin Campus, Melaka, Malaysia
  • Raihah Aminuddin College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Melaka Branch, Jasin Campus, Melaka, Malaysia
  • Nor Afirdaus Zainal Abidin College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Melaka Branch, Jasin Campus, Melaka, Malaysia
  • Lala Septem Riza Department of Computer Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia

DOI:

https://doi.org/10.37934/araset.37.1.139150

Keywords:

Online Food Delivery, Twitter Sentiment Analysis, Aspect-Based, Naïve Bayes

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.

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Author Biographies

Khyrina Airin Fariza Abu Samah, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Melaka Branch, Jasin Campus, Melaka, Malaysia

khyrina783@uitm.edu.my

Nur Shahirah Jailani, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Melaka Branch, Jasin Campus, Melaka, Malaysia

nshahirah567@gmail.com

Raseeda Hamzah, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Melaka Branch, Jasin Campus, Melaka, Malaysia

raseeda@uitm.edu.my

Raihah Aminuddin, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Melaka Branch, Jasin Campus, Melaka, Malaysia

raihah1@uitm.edu.my

Nor Afirdaus Zainal Abidin, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Melaka Branch, Jasin Campus, Melaka, Malaysia

afirdaus@uitm.edu.my

Lala Septem Riza, Department of Computer Science Education, Universitas Pendidikan Indonesia, Bandung, Indonesia

lala.s.riza@upi.edu

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Published

2024-01-09

How to Cite

Khyrina Airin Fariza Abu Samah, Nur Shahirah Jailani, Raseeda Hamzah, Raihah Aminuddin, Nor Afirdaus Zainal Abidin, & Lala Septem Riza. (2024). Aspect-Based Classification and Visualization of Twitter Sentiment Analysis Towards Online Food Delivery Services in Malaysia. Journal of Advanced Research in Applied Sciences and Engineering Technology, 37(1), 139–150. https://doi.org/10.37934/araset.37.1.139150

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