Sentiment analysis of women’s sportswear brands review on e-commerce using long short-term memory / Nur Syahirah Jaafar

The sentiment analysis of women's sportswear brands on e-commerce platforms using long short-term memory (LSTM) networks is explored in this study. Evaluating sentiment towards brands is crucial for understanding consumer preferences and market trends. The study focuses on sentiment analysis as...

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Main Author:	Jaafar, Nur Syahirah
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/96276/1/96276.pdf
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id	my-uitm-ir.96276
record_format	uketd_dc
spelling	my-uitm-ir.962762024-06-04T07:20:19Z Sentiment analysis of women’s sportswear brands review on e-commerce using long short-term memory / Nur Syahirah Jaafar 2024 Jaafar, Nur Syahirah Neural networks (Computer science) The sentiment analysis of women's sportswear brands on e-commerce platforms using long short-term memory (LSTM) networks is explored in this study. Evaluating sentiment towards brands is crucial for understanding consumer preferences and market trends. The study focuses on sentiment analysis as it pertains to women's sportswear brands, aiming to provide insights into customer satisfaction and perception. Effective sentiment analysis enables businesses to make informed decisions regarding product development, marketing strategies, and brand positioning. Leveraging LSTM networks, known for their ability to capture sequential patterns in data, the study achieves a comprehensive understanding of customer sentiment towards women's sportswear brands. Through meticulous data pre-processing and analysis techniques, the study offers valuable insights into consumer behaviour and preferences in the e-commerce domain. Utilizing the powerful LSTM model known for its proficiency in learning model layer representations from data processing, the system achieves an impressive accuracy of 90% and above 2024 Thesis https://ir.uitm.edu.my/id/eprint/96276/ https://ir.uitm.edu.my/id/eprint/96276/1/96276.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Media Abdul Malek, Mohamad Affendi
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Abdul Malek, Mohamad Affendi
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Jaafar, Nur Syahirah Sentiment analysis of women’s sportswear brands review on e-commerce using long short-term memory / Nur Syahirah Jaafar
description	The sentiment analysis of women's sportswear brands on e-commerce platforms using long short-term memory (LSTM) networks is explored in this study. Evaluating sentiment towards brands is crucial for understanding consumer preferences and market trends. The study focuses on sentiment analysis as it pertains to women's sportswear brands, aiming to provide insights into customer satisfaction and perception. Effective sentiment analysis enables businesses to make informed decisions regarding product development, marketing strategies, and brand positioning. Leveraging LSTM networks, known for their ability to capture sequential patterns in data, the study achieves a comprehensive understanding of customer sentiment towards women's sportswear brands. Through meticulous data pre-processing and analysis techniques, the study offers valuable insights into consumer behaviour and preferences in the e-commerce domain. Utilizing the powerful LSTM model known for its proficiency in learning model layer representations from data processing, the system achieves an impressive accuracy of 90% and above
format	Thesis
qualification_level	Bachelor degree
author	Jaafar, Nur Syahirah
author_facet	Jaafar, Nur Syahirah
author_sort	Jaafar, Nur Syahirah
title	Sentiment analysis of women’s sportswear brands review on e-commerce using long short-term memory / Nur Syahirah Jaafar
title_short	Sentiment analysis of women’s sportswear brands review on e-commerce using long short-term memory / Nur Syahirah Jaafar
title_full	Sentiment analysis of women’s sportswear brands review on e-commerce using long short-term memory / Nur Syahirah Jaafar
title_fullStr	Sentiment analysis of women’s sportswear brands review on e-commerce using long short-term memory / Nur Syahirah Jaafar
title_full_unstemmed	Sentiment analysis of women’s sportswear brands review on e-commerce using long short-term memory / Nur Syahirah Jaafar
title_sort	sentiment analysis of women’s sportswear brands review on e-commerce using long short-term memory / nur syahirah jaafar
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Media
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96276/1/96276.pdf
_version_	1804889982020616192

Sentiment analysis of women’s sportswear brands review on e-commerce using long short-term memory / Nur Syahirah Jaafar

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