Twitter sentiment analysis of Malaysian fast food restaurant chains: a novel approach to understand customer perception using Naïve Bayes / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan and Khairul Nizam Abd Halim
Social media has emerged as a prominent platform for users to share ideas, opinions, and thoughts, leading to an increase in consumers expressing their product feedback through these channels rather than providing direct feedback to companies. Fast food has gained popularity in recent years due to i...
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Faculty of Computer and Mathematical Sciences
2023
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my.uitm.ir.938812024-05-28T08:27:51Z https://ir.uitm.edu.my/id/eprint/93881/ Twitter sentiment analysis of Malaysian fast food restaurant chains: a novel approach to understand customer perception using Naïve Bayes / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan and Khairul Nizam Abd Halim Muhd Zahidi Ridzuan, Muhammad Hafeez Hakimi Abd Halim, hairul Nizam Integer programming Social media has emerged as a prominent platform for users to share ideas, opinions, and thoughts, leading to an increase in consumers expressing their product feedback through these channels rather than providing direct feedback to companies. Fast food has gained popularity in recent years due to its affordability, tastiness, and convenience. However, there is a lack of a dedicated platform for customers to access comprehensive reviews of fast food restaurants in Malaysia, resulting in time-consuming processes when trying to read online reviews. This project introduces a web-based system that uses Twitter sentiment analysis to visualize reviews of Malaysian fast food restaurants. It employs Naïve Bayes algorithm and Plotly library in Python to provide insights into customer perceptions, enhancing the fast food brand experience in Malaysia. This system introduces a comprehensive solution to understand restaurant sentiments by employing a visualization dashboard and conducting comparative analysis between various companies. Moreover, it empowers users to analyze their own Twitter data by utilizing a sentiment analyzer, which predicts the sentiments associated with the provided textual data. Faculty of Computer and Mathematical Sciences 2023 Book Section NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/93881/1/93881.pdf Twitter sentiment analysis of Malaysian fast food restaurant chains: a novel approach to understand customer perception using Naïve Bayes / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan and Khairul Nizam Abd Halim. (2023) In: International Jasin Multimedia & Computer Science Invention and Innovation Exhibition (i-JaMCSIIX 2023). Faculty of Computer and Mathematical Sciences, Kampus Jasin, p. 24. (Submitted) |
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Integer programming Muhd Zahidi Ridzuan, Muhammad Hafeez Hakimi Abd Halim, hairul Nizam Twitter sentiment analysis of Malaysian fast food restaurant chains: a novel approach to understand customer perception using Naïve Bayes / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan and Khairul Nizam Abd Halim |
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Social media has emerged as a prominent platform for users to share ideas, opinions, and thoughts, leading to an increase in consumers expressing their product feedback through these channels rather than providing direct feedback to companies. Fast food has gained popularity in recent years due to its affordability, tastiness, and convenience. However, there is a lack of a dedicated platform for customers to access comprehensive reviews of fast food restaurants in Malaysia, resulting in time-consuming processes when trying to read online reviews. This project introduces a web-based system that uses Twitter sentiment analysis to visualize reviews of Malaysian fast food restaurants. It employs Naïve Bayes algorithm and Plotly library in Python to provide insights into customer perceptions, enhancing the fast food brand experience in Malaysia. This system introduces a comprehensive solution to understand restaurant sentiments by employing a visualization dashboard and conducting comparative analysis between various companies. Moreover, it empowers users to analyze their own Twitter data by utilizing a sentiment analyzer, which predicts the sentiments associated with the provided textual data. |
format |
Book Section |
author |
Muhd Zahidi Ridzuan, Muhammad Hafeez Hakimi Abd Halim, hairul Nizam |
author_facet |
Muhd Zahidi Ridzuan, Muhammad Hafeez Hakimi Abd Halim, hairul Nizam |
author_sort |
Muhd Zahidi Ridzuan, Muhammad Hafeez Hakimi |
title |
Twitter sentiment analysis of Malaysian fast food restaurant chains: a novel approach to understand customer perception using Naïve Bayes / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan and Khairul Nizam Abd Halim |
title_short |
Twitter sentiment analysis of Malaysian fast food restaurant chains: a novel approach to understand customer perception using Naïve Bayes / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan and Khairul Nizam Abd Halim |
title_full |
Twitter sentiment analysis of Malaysian fast food restaurant chains: a novel approach to understand customer perception using Naïve Bayes / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan and Khairul Nizam Abd Halim |
title_fullStr |
Twitter sentiment analysis of Malaysian fast food restaurant chains: a novel approach to understand customer perception using Naïve Bayes / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan and Khairul Nizam Abd Halim |
title_full_unstemmed |
Twitter sentiment analysis of Malaysian fast food restaurant chains: a novel approach to understand customer perception using Naïve Bayes / Muhammad Hafeez Hakimi Muhd Zahidi Ridzuan and Khairul Nizam Abd Halim |
title_sort |
twitter sentiment analysis of malaysian fast food restaurant chains: a novel approach to understand customer perception using naïve bayes / muhammad hafeez hakimi muhd zahidi ridzuan and khairul nizam abd halim |
publisher |
Faculty of Computer and Mathematical Sciences |
publishDate |
2023 |
url |
https://ir.uitm.edu.my/id/eprint/93881/1/93881.pdf https://ir.uitm.edu.my/id/eprint/93881/ |
_version_ |
1800726585948504064 |
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13.18916 |