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Classification and visualization of Malaysian fast food restaurant chain based on twitter sentiment analysis / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan

Social media refers to a computer-based technology where users may create online communities to share ideas, opinions, and thoughts. Due to the transparency of social media, consumers are more likely to express their thoughts about a product on social media instead of providing direct feedback to th...

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Main Author: Muhd Zahidi Ridzuan, Muhammad Hafeez Hakimi
Format: Thesis
Language:English
Published: 2023
Subjects:
Integer programming
Online Access:https://ir.uitm.edu.my/id/eprint/89086/1/89086.pdf
https://ir.uitm.edu.my/id/eprint/89086/
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spelling my.uitm.ir.890862024-09-29T04:29:56Z https://ir.uitm.edu.my/id/eprint/89086/ Classification and visualization of Malaysian fast food restaurant chain based on twitter sentiment analysis / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan Muhd Zahidi Ridzuan, Muhammad Hafeez Hakimi Integer programming Social media refers to a computer-based technology where users may create online communities to share ideas, opinions, and thoughts. Due to the transparency of social media, consumers are more likely to express their thoughts about a product on social media instead of providing direct feedback to the company. Fast food has become increasingly popular in recent years due to its affordability, tastiness, and convenience. However, there is currently no dedicated platform for customers to access reviews for all fast food restaurants in Malaysia. Customers may also face the challenge of time-consuming processes when trying to read online reviews. Based on these challenges, the goals of this project are to design a web system that can visualize online reviews of Malaysian fast food restaurants using Twitter sentiment analysis. This project uses an algorithm called Naïve Bayes and the visualization is aided by the Plotly library in Python. The methodology used in this project is known as the Modified Waterfall Model, which consists of four primary phases: requirement analysis, design, implementation, and testing. Initially, the data was pre-processed, followed by the development, and testing of a classifier model using real-world data. Functionality testing demonstrated that the system achieved prediction accuracies of 79.19% for English and 76.98% for Malay, based on training and testing data. The usability testing was conducted using System Usability Scale (SUS) and achieved an average final score of 93.13%. In conclusion, this project has developed a system that could benefit all fast food restaurants customers in Malaysia by providing an analysis of reviews. However, there are areas for improvement, such as expanding the system to include other social media platforms as data sources and training the model with a comprehensive dictionary of Malay slangs and common abbreviations. 2023 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/89086/1/89086.pdf Classification and visualization of Malaysian fast food restaurant chain based on twitter sentiment analysis / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan. (2023) Degree thesis, thesis, Universiti Teknologi MARA, Melaka. <http://terminalib.uitm.edu.my/89086.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Integer programming
spellingShingle Integer programming
Muhd Zahidi Ridzuan, Muhammad Hafeez Hakimi
Classification and visualization of Malaysian fast food restaurant chain based on twitter sentiment analysis / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan
description Social media refers to a computer-based technology where users may create online communities to share ideas, opinions, and thoughts. Due to the transparency of social media, consumers are more likely to express their thoughts about a product on social media instead of providing direct feedback to the company. Fast food has become increasingly popular in recent years due to its affordability, tastiness, and convenience. However, there is currently no dedicated platform for customers to access reviews for all fast food restaurants in Malaysia. Customers may also face the challenge of time-consuming processes when trying to read online reviews. Based on these challenges, the goals of this project are to design a web system that can visualize online reviews of Malaysian fast food restaurants using Twitter sentiment analysis. This project uses an algorithm called Naïve Bayes and the visualization is aided by the Plotly library in Python. The methodology used in this project is known as the Modified Waterfall Model, which consists of four primary phases: requirement analysis, design, implementation, and testing. Initially, the data was pre-processed, followed by the development, and testing of a classifier model using real-world data. Functionality testing demonstrated that the system achieved prediction accuracies of 79.19% for English and 76.98% for Malay, based on training and testing data. The usability testing was conducted using System Usability Scale (SUS) and achieved an average final score of 93.13%. In conclusion, this project has developed a system that could benefit all fast food restaurants customers in Malaysia by providing an analysis of reviews. However, there are areas for improvement, such as expanding the system to include other social media platforms as data sources and training the model with a comprehensive dictionary of Malay slangs and common abbreviations.
format Thesis
author Muhd Zahidi Ridzuan, Muhammad Hafeez Hakimi
author_facet Muhd Zahidi Ridzuan, Muhammad Hafeez Hakimi
author_sort Muhd Zahidi Ridzuan, Muhammad Hafeez Hakimi
title Classification and visualization of Malaysian fast food restaurant chain based on twitter sentiment analysis / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan
title_short Classification and visualization of Malaysian fast food restaurant chain based on twitter sentiment analysis / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan
title_full Classification and visualization of Malaysian fast food restaurant chain based on twitter sentiment analysis / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan
title_fullStr Classification and visualization of Malaysian fast food restaurant chain based on twitter sentiment analysis / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan
title_full_unstemmed Classification and visualization of Malaysian fast food restaurant chain based on twitter sentiment analysis / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan
title_sort classification and visualization of malaysian fast food restaurant chain based on twitter sentiment analysis / muhammad hafeez hakimi muhd zahidi ridzuan
publishDate 2023
url https://ir.uitm.edu.my/id/eprint/89086/1/89086.pdf
https://ir.uitm.edu.my/id/eprint/89086/
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score 13.211869

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