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Sentiment Analysis For E-Commerce Clothing Reviews Using Bidirectional Recurrent Neural Network

Today's marketing methods place a high priority on comprehending client emotions. Companies will gain insight into how customers view their goods and/or services, and they will get ideas on how to enhance their offerings. Traditional methods of sales and business are not as effective as the e-c...

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Main Author: Muhammad Farhan Firdaus, Hairol Zaman
Format: Undergraduates Project Papers
Language:English
Published: 2023
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/40875/1/CB20153.pdf
http://umpir.ump.edu.my/id/eprint/40875/
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spelling my.ump.umpir.408752024-04-03T07:43:44Z http://umpir.ump.edu.my/id/eprint/40875/ Sentiment Analysis For E-Commerce Clothing Reviews Using Bidirectional Recurrent Neural Network Muhammad Farhan Firdaus, Hairol Zaman QA75 Electronic computers. Computer science Today's marketing methods place a high priority on comprehending client emotions. Companies will gain insight into how customers view their goods and/or services, and they will get ideas on how to enhance their offerings. Traditional methods of sales and business are not as effective as the e-commerce approach. It is such a hassle for customers to walk into the retail store and purchase their product needs. It is a waste of time and energy for today which world is full of technology. This study makes an effort to comprehend the relationship between several factors in customer reviews on an online store selling women's clothes. It also aims to categorize each review according to whether it recommends the product under consideration and whether it expresses a positive, negative, or neutral attitude. Thus, this study proposed a bidirectional recurrent neural network (RNN) with a long-short-term memory unit (LSTM) for sentiment classification. Results have indicated that a major predictor of a high sentiment score is a recommendation, and vice versa. Ratings in product reviews, on the other hand, are hazy predictors of sentiment scores. Additionally, we discovered that the bidirectional LSTM achieved an F1-score of 0.93 for sentiment classification. 2023-01 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40875/1/CB20153.pdf Muhammad Farhan Firdaus, Hairol Zaman (2023) Sentiment Analysis For E-Commerce Clothing Reviews Using Bidirectional Recurrent Neural Network. Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah.
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Muhammad Farhan Firdaus, Hairol Zaman
Sentiment Analysis For E-Commerce Clothing Reviews Using Bidirectional Recurrent Neural Network
description Today's marketing methods place a high priority on comprehending client emotions. Companies will gain insight into how customers view their goods and/or services, and they will get ideas on how to enhance their offerings. Traditional methods of sales and business are not as effective as the e-commerce approach. It is such a hassle for customers to walk into the retail store and purchase their product needs. It is a waste of time and energy for today which world is full of technology. This study makes an effort to comprehend the relationship between several factors in customer reviews on an online store selling women's clothes. It also aims to categorize each review according to whether it recommends the product under consideration and whether it expresses a positive, negative, or neutral attitude. Thus, this study proposed a bidirectional recurrent neural network (RNN) with a long-short-term memory unit (LSTM) for sentiment classification. Results have indicated that a major predictor of a high sentiment score is a recommendation, and vice versa. Ratings in product reviews, on the other hand, are hazy predictors of sentiment scores. Additionally, we discovered that the bidirectional LSTM achieved an F1-score of 0.93 for sentiment classification.
format Undergraduates Project Papers
author Muhammad Farhan Firdaus, Hairol Zaman
author_facet Muhammad Farhan Firdaus, Hairol Zaman
author_sort Muhammad Farhan Firdaus, Hairol Zaman
title Sentiment Analysis For E-Commerce Clothing Reviews Using Bidirectional Recurrent Neural Network
title_short Sentiment Analysis For E-Commerce Clothing Reviews Using Bidirectional Recurrent Neural Network
title_full Sentiment Analysis For E-Commerce Clothing Reviews Using Bidirectional Recurrent Neural Network
title_fullStr Sentiment Analysis For E-Commerce Clothing Reviews Using Bidirectional Recurrent Neural Network
title_full_unstemmed Sentiment Analysis For E-Commerce Clothing Reviews Using Bidirectional Recurrent Neural Network
title_sort sentiment analysis for e-commerce clothing reviews using bidirectional recurrent neural network
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40875/1/CB20153.pdf
http://umpir.ump.edu.my/id/eprint/40875/
_version_ 1822924261348605952
score 13.235362

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