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Aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under COVID‐19 pandemic in India

The COVID‐19 pandemic has a significant impact on the global economy and health. While the pandemic continues to cause casualties in millions, many countries have gone under lockdown. During this period, people have to stay within walls and become more addicted towards social networks. They express...

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Autores principales: Dutta, Rakesh, Das, Nilanjana, Majumder, Mukta, Jana, Biswapati
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9874458/
https://www.ncbi.nlm.nih.gov/pubmed/36712294
http://dx.doi.org/10.1049/cit2.12144
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author Dutta, Rakesh
Das, Nilanjana
Majumder, Mukta
Jana, Biswapati
author_facet Dutta, Rakesh
Das, Nilanjana
Majumder, Mukta
Jana, Biswapati
author_sort Dutta, Rakesh
collection PubMed
description The COVID‐19 pandemic has a significant impact on the global economy and health. While the pandemic continues to cause casualties in millions, many countries have gone under lockdown. During this period, people have to stay within walls and become more addicted towards social networks. They express their emotions and sympathy via these online platforms. Thus, popular social media (Twitter and Facebook) have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID‐19‐related issues. We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases. The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus. India‐specific COVID‐19 tweets have been annotated, for analysing the sentiment of common public. To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35% for Lockdown and 83.33% for Unlock data set. The suggested method outperforms many of the contemporary approaches (long short‐term memory, Bi‐directional long short‐term memory, Gated Recurrent Unit etc.). This study highlights the public sentiment on lockdown and stepwise unlocks, imposed by the Indian Government on various aspects during the Corona outburst.
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spelling pubmed-98744582023-01-25 Aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under COVID‐19 pandemic in India Dutta, Rakesh Das, Nilanjana Majumder, Mukta Jana, Biswapati CAAI Trans Intell Technol Original Research The COVID‐19 pandemic has a significant impact on the global economy and health. While the pandemic continues to cause casualties in millions, many countries have gone under lockdown. During this period, people have to stay within walls and become more addicted towards social networks. They express their emotions and sympathy via these online platforms. Thus, popular social media (Twitter and Facebook) have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID‐19‐related issues. We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases. The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus. India‐specific COVID‐19 tweets have been annotated, for analysing the sentiment of common public. To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35% for Lockdown and 83.33% for Unlock data set. The suggested method outperforms many of the contemporary approaches (long short‐term memory, Bi‐directional long short‐term memory, Gated Recurrent Unit etc.). This study highlights the public sentiment on lockdown and stepwise unlocks, imposed by the Indian Government on various aspects during the Corona outburst. John Wiley and Sons Inc. 2022-10-19 /pmc/articles/PMC9874458/ /pubmed/36712294 http://dx.doi.org/10.1049/cit2.12144 Text en © 2022 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Research
Dutta, Rakesh
Das, Nilanjana
Majumder, Mukta
Jana, Biswapati
Aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under COVID‐19 pandemic in India
title Aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under COVID‐19 pandemic in India
title_full Aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under COVID‐19 pandemic in India
title_fullStr Aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under COVID‐19 pandemic in India
title_full_unstemmed Aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under COVID‐19 pandemic in India
title_short Aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under COVID‐19 pandemic in India
title_sort aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under covid‐19 pandemic in india
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9874458/
https://www.ncbi.nlm.nih.gov/pubmed/36712294
http://dx.doi.org/10.1049/cit2.12144
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