Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic

The Covid-19 pandemic has disrupted the world economy and significantly influenced the tourism industry. Millions of people have shared their emotions, views, facts, and circumstances on numerous social media platforms, which has resulted in a massive flow of information. The high-density social med...

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Autores principales:	Ram Krishn Mishra, Siddhaling Urolagin, J. Angel Arul Jothi, Ashwin Sanjay Neogi, Nishad Nawaz
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	social media tourism text analysis deep learning topic modeling sentiment analysis Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/da63d87333934d989a352c3a04a1c2d7
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id	oai:doaj.org-article:da63d87333934d989a352c3a04a1c2d7
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spelling	oai:doaj.org-article:da63d87333934d989a352c3a04a1c2d72021-11-05T16:47:39ZDeep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic2624-989810.3389/fcomp.2021.775368https://doaj.org/article/da63d87333934d989a352c3a04a1c2d72021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcomp.2021.775368/fullhttps://doaj.org/toc/2624-9898The Covid-19 pandemic has disrupted the world economy and significantly influenced the tourism industry. Millions of people have shared their emotions, views, facts, and circumstances on numerous social media platforms, which has resulted in a massive flow of information. The high-density social media data has drawn many researchers to extract valuable information and understand the user’s emotions during the pandemic time. The research looks at the data collected from the micro-blogging site Twitter for the tourism sector, emphasizing sub-domains hospitality and healthcare. The sentiment of approximately 20,000 tweets have been calculated using Valence Aware Dictionary for Sentiment Reasoning (VADER) model. Furthermore, topic modeling was used to reveal certain hidden themes and determine the narrative and direction of the topics related to tourism healthcare, and hospitality. Topic modeling also helped us to identify inter-cluster similar terms and analyzing the flow of information from a group of a similar opinion. Finally, a cutting-edge deep learning classification model was used with different epoch sizes of the dataset to anticipate and classify the people’s feelings. The deep learning model has been tested with multiple parameters such as training set accuracy, test set accuracy, validation loss, validation accuracy, etc., and resulted in more than a 90% in training set accuracy tourism hospitality and healthcare reported 80.9 and 78.7% respectively on test set accuracy.Ram Krishn MishraSiddhaling UrolaginJ. Angel Arul JothiAshwin Sanjay NeogiNishad NawazFrontiers Media S.A.articlesocial media tourismtext analysisdeep learningtopic modelingsentiment analysisElectronic computers. Computer scienceQA75.5-76.95ENFrontiers in Computer Science, Vol 3 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	social media tourism text analysis deep learning topic modeling sentiment analysis Electronic computers. Computer science QA75.5-76.95
spellingShingle	social media tourism text analysis deep learning topic modeling sentiment analysis Electronic computers. Computer science QA75.5-76.95 Ram Krishn Mishra Siddhaling Urolagin J. Angel Arul Jothi Ashwin Sanjay Neogi Nishad Nawaz Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic
description	The Covid-19 pandemic has disrupted the world economy and significantly influenced the tourism industry. Millions of people have shared their emotions, views, facts, and circumstances on numerous social media platforms, which has resulted in a massive flow of information. The high-density social media data has drawn many researchers to extract valuable information and understand the user’s emotions during the pandemic time. The research looks at the data collected from the micro-blogging site Twitter for the tourism sector, emphasizing sub-domains hospitality and healthcare. The sentiment of approximately 20,000 tweets have been calculated using Valence Aware Dictionary for Sentiment Reasoning (VADER) model. Furthermore, topic modeling was used to reveal certain hidden themes and determine the narrative and direction of the topics related to tourism healthcare, and hospitality. Topic modeling also helped us to identify inter-cluster similar terms and analyzing the flow of information from a group of a similar opinion. Finally, a cutting-edge deep learning classification model was used with different epoch sizes of the dataset to anticipate and classify the people’s feelings. The deep learning model has been tested with multiple parameters such as training set accuracy, test set accuracy, validation loss, validation accuracy, etc., and resulted in more than a 90% in training set accuracy tourism hospitality and healthcare reported 80.9 and 78.7% respectively on test set accuracy.
format	article
author	Ram Krishn Mishra Siddhaling Urolagin J. Angel Arul Jothi Ashwin Sanjay Neogi Nishad Nawaz
author_facet	Ram Krishn Mishra Siddhaling Urolagin J. Angel Arul Jothi Ashwin Sanjay Neogi Nishad Nawaz
author_sort	Ram Krishn Mishra
title	Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic
title_short	Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic
title_full	Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic
title_fullStr	Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic
title_full_unstemmed	Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic
title_sort	deep learning-based sentiment analysis and topic modeling on tourism during covid-19 pandemic
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/da63d87333934d989a352c3a04a1c2d7
work_keys_str_mv	AT ramkrishnmishra deeplearningbasedsentimentanalysisandtopicmodelingontourismduringcovid19pandemic AT siddhalingurolagin deeplearningbasedsentimentanalysisandtopicmodelingontourismduringcovid19pandemic AT jangelaruljothi deeplearningbasedsentimentanalysisandtopicmodelingontourismduringcovid19pandemic AT ashwinsanjayneogi deeplearningbasedsentimentanalysisandtopicmodelingontourismduringcovid19pandemic AT nishadnawaz deeplearningbasedsentimentanalysisandtopicmodelingontourismduringcovid19pandemic
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Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic

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