Mobile telecommunication recommendation system using collaborative filtering and sentiment analysis via Twitter / Norzarifah Farina Zulkiflee

This study presents a web-based recommender system designed to address the challenge of selecting Malaysia's most suitable mobile telecommunication provider. The system combines Twitter sentiment analysis and collaborative filtering to provide personalized and informed recommendations to users....

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Main Author:	Zulkiflee, Norzarifah Farina
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Telecommunication
Online Access:	https://ir.uitm.edu.my/id/eprint/89022/1/89022.pdf
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id	my-uitm-ir.89022
record_format	uketd_dc
spelling	my-uitm-ir.890222024-03-19T07:08:14Z Mobile telecommunication recommendation system using collaborative filtering and sentiment analysis via Twitter / Norzarifah Farina Zulkiflee 2023 Zulkiflee, Norzarifah Farina Telecommunication This study presents a web-based recommender system designed to address the challenge of selecting Malaysia's most suitable mobile telecommunication provider. The system combines Twitter sentiment analysis and collaborative filtering to provide personalized and informed recommendations to users. The background analysis reveals users' difficulty finding the right option amongst numerous providers while existing recommendation methods prove costly and inefficient. The research aims to design and implement a solution that leverages Twitter sentiment analysis to gather real-time user opinions and employs collaborative filtering to offer personalized options. Following a modified waterfall model, the study gathers requirements, conceptualizes the system, and collects data by analyzing tweets expressing user sentiments toward mobile telecommunication providers. The results demonstrate the system's effectiveness, achieving an impressive sentiment analysis accuracy of approximately 87.89% using the Logistic Regression model. The collaborative filtering approach generates personalized recommendations based on user interactions, assisting users in making well-informed decisions. In conclusion, the web-based recommender system successfully combines Twitter sentiment analysis and collaborative filtering, providing valuable assistance to users in selecting the best mobile telecommunication provider tailored to their needs in the competitive Malaysian market. Future research includes enriching the dataset, integrating hybrid recommendation techniques, implementing real-time sentiment analysis, and enhancing user feedback integration for continuous improvement. A user-friendly mobile application is also suggested to improve accessibility and user experience. 2023 Thesis https://ir.uitm.edu.my/id/eprint/89022/ https://ir.uitm.edu.my/id/eprint/89022/1/89022.pdf text en public degree Universiti Teknologi MARA, Melaka College of Computing, Informatics and Mathematics
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Telecommunication
spellingShingle	Telecommunication Zulkiflee, Norzarifah Farina Mobile telecommunication recommendation system using collaborative filtering and sentiment analysis via Twitter / Norzarifah Farina Zulkiflee
description	This study presents a web-based recommender system designed to address the challenge of selecting Malaysia's most suitable mobile telecommunication provider. The system combines Twitter sentiment analysis and collaborative filtering to provide personalized and informed recommendations to users. The background analysis reveals users' difficulty finding the right option amongst numerous providers while existing recommendation methods prove costly and inefficient. The research aims to design and implement a solution that leverages Twitter sentiment analysis to gather real-time user opinions and employs collaborative filtering to offer personalized options. Following a modified waterfall model, the study gathers requirements, conceptualizes the system, and collects data by analyzing tweets expressing user sentiments toward mobile telecommunication providers. The results demonstrate the system's effectiveness, achieving an impressive sentiment analysis accuracy of approximately 87.89% using the Logistic Regression model. The collaborative filtering approach generates personalized recommendations based on user interactions, assisting users in making well-informed decisions. In conclusion, the web-based recommender system successfully combines Twitter sentiment analysis and collaborative filtering, providing valuable assistance to users in selecting the best mobile telecommunication provider tailored to their needs in the competitive Malaysian market. Future research includes enriching the dataset, integrating hybrid recommendation techniques, implementing real-time sentiment analysis, and enhancing user feedback integration for continuous improvement. A user-friendly mobile application is also suggested to improve accessibility and user experience.
format	Thesis
qualification_level	Bachelor degree
author	Zulkiflee, Norzarifah Farina
author_facet	Zulkiflee, Norzarifah Farina
author_sort	Zulkiflee, Norzarifah Farina
title	Mobile telecommunication recommendation system using collaborative filtering and sentiment analysis via Twitter / Norzarifah Farina Zulkiflee
title_short	Mobile telecommunication recommendation system using collaborative filtering and sentiment analysis via Twitter / Norzarifah Farina Zulkiflee
title_full	Mobile telecommunication recommendation system using collaborative filtering and sentiment analysis via Twitter / Norzarifah Farina Zulkiflee
title_fullStr	Mobile telecommunication recommendation system using collaborative filtering and sentiment analysis via Twitter / Norzarifah Farina Zulkiflee
title_full_unstemmed	Mobile telecommunication recommendation system using collaborative filtering and sentiment analysis via Twitter / Norzarifah Farina Zulkiflee
title_sort	mobile telecommunication recommendation system using collaborative filtering and sentiment analysis via twitter / norzarifah farina zulkiflee
granting_institution	Universiti Teknologi MARA, Melaka
granting_department	College of Computing, Informatics and Mathematics
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/89022/1/89022.pdf
_version_	1794192189741334528

Mobile telecommunication recommendation system using collaborative filtering and sentiment analysis via Twitter / Norzarifah Farina Zulkiflee

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