Sentiment Analysis for Video on Demand Application User Satisfaction with Long Short Term Memory Model

Customer reviews are important information that can be used by providers of goods or services to maintain customer loyalty, understand customer feelings and analyze the business competition. Users use customer feedback on social media or e-commerce as material for consideration before using or buyin...

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Autores principales:	Khayatun Nufus Gina, Mustafid Mustafid, Gernowo Rahmat
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/1fef7db797934a05bd8a6bc5a18f799d
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spelling	oai:doaj.org-article:1fef7db797934a05bd8a6bc5a18f799d2021-11-08T15:19:57ZSentiment Analysis for Video on Demand Application User Satisfaction with Long Short Term Memory Model2267-124210.1051/e3sconf/202131705031https://doaj.org/article/1fef7db797934a05bd8a6bc5a18f799d2021-01-01T00:00:00Zhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2021/93/e3sconf_icenis2021_05031.pdfhttps://doaj.org/toc/2267-1242Customer reviews are important information that can be used by providers of goods or services to maintain customer loyalty, understand customer feelings and analyze the business competition. Users use customer feedback on social media or e-commerce as material for consideration before using or buying products. This study aims to conduct a sentiment analysis for user satisfaction of the Video on Demand application in Indonesia with the Long Short-Term Memory (LSTM) model. LSTM, which is a deep learning method that is widely implemented in natural language processing research. Sentiment analysis is applied to find out how customers feel about products on the market. The study results indicate that the LSTM model's implementation for sentiment analysis using two positive and negative labels obtained the value of precision 73.81%, recall 73.81%, f1 score 73.81%. In addition, the accuracy value obtained is 73.90% can be used as a consideration for the company in knowing user sentiment to meet the expectations and desires of customers and keep customers using the service.Khayatun Nufus GinaMustafid MustafidGernowo RahmatEDP SciencesarticleEnvironmental sciencesGE1-350ENFRE3S Web of Conferences, Vol 317, p 05031 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Environmental sciences GE1-350
spellingShingle	Environmental sciences GE1-350 Khayatun Nufus Gina Mustafid Mustafid Gernowo Rahmat Sentiment Analysis for Video on Demand Application User Satisfaction with Long Short Term Memory Model
description	Customer reviews are important information that can be used by providers of goods or services to maintain customer loyalty, understand customer feelings and analyze the business competition. Users use customer feedback on social media or e-commerce as material for consideration before using or buying products. This study aims to conduct a sentiment analysis for user satisfaction of the Video on Demand application in Indonesia with the Long Short-Term Memory (LSTM) model. LSTM, which is a deep learning method that is widely implemented in natural language processing research. Sentiment analysis is applied to find out how customers feel about products on the market. The study results indicate that the LSTM model's implementation for sentiment analysis using two positive and negative labels obtained the value of precision 73.81%, recall 73.81%, f1 score 73.81%. In addition, the accuracy value obtained is 73.90% can be used as a consideration for the company in knowing user sentiment to meet the expectations and desires of customers and keep customers using the service.
format	article
author	Khayatun Nufus Gina Mustafid Mustafid Gernowo Rahmat
author_facet	Khayatun Nufus Gina Mustafid Mustafid Gernowo Rahmat
author_sort	Khayatun Nufus Gina
title	Sentiment Analysis for Video on Demand Application User Satisfaction with Long Short Term Memory Model
title_short	Sentiment Analysis for Video on Demand Application User Satisfaction with Long Short Term Memory Model
title_full	Sentiment Analysis for Video on Demand Application User Satisfaction with Long Short Term Memory Model
title_fullStr	Sentiment Analysis for Video on Demand Application User Satisfaction with Long Short Term Memory Model
title_full_unstemmed	Sentiment Analysis for Video on Demand Application User Satisfaction with Long Short Term Memory Model
title_sort	sentiment analysis for video on demand application user satisfaction with long short term memory model
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/1fef7db797934a05bd8a6bc5a18f799d
work_keys_str_mv	AT khayatunnufusgina sentimentanalysisforvideoondemandapplicationusersatisfactionwithlongshorttermmemorymodel AT mustafidmustafid sentimentanalysisforvideoondemandapplicationusersatisfactionwithlongshorttermmemorymodel AT gernoworahmat sentimentanalysisforvideoondemandapplicationusersatisfactionwithlongshorttermmemorymodel
_version_	1718441830217940992

Sentiment Analysis for Video on Demand Application User Satisfaction with Long Short Term Memory Model

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