Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic

Social media platforms are increasingly being used to communicate information, something which has only intensified during the pandemic. News portals and governments are also increasing attention to digital communications, announcements and response or reaction monitoring. Twitter, as one of the lar...

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Autores principales:	László Nemes, Attila Kiss
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sentiment analysis recurrent neural network information extraction named entity recognition social media Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/34f58199aaf946c1a034c324cb304ede
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spelling	oai:doaj.org-article:34f58199aaf946c1a034c324cb304ede2021-11-25T16:42:57ZInformation Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic10.3390/app1122110172076-3417https://doaj.org/article/34f58199aaf946c1a034c324cb304ede2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/11017https://doaj.org/toc/2076-3417Social media platforms are increasingly being used to communicate information, something which has only intensified during the pandemic. News portals and governments are also increasing attention to digital communications, announcements and response or reaction monitoring. Twitter, as one of the largest social networking sites, which has become even more important in the communication of information during the pandemic, provides space for a lot of different opinions and news, with many discussions as well. In this paper, we look at the sentiments of people and we use tweets to determine how people have related to COVID-19 over a given period of time. These sentiment analyses are augmented with information extraction and named entity recognition to get an even more comprehensive picture. The sentiment analysis is based on the ’Bidirectional encoder representations from transformers’ (BERT) model, which is the basic measurement model for the comparisons. We consider BERT as the baseline and compare the results with the RNN, NLTK and TextBlob sentiment analyses. The RNN results are significantly closer to the benchmark results given by BERT, both models are able to categorize all tweets without a single tweet fall into the neutral category. Then, via a deeper analysis of these results, we can get an even more concise picture of people’s emotional state in the given period of time. The data from these analyses further support the emotional categories, and provide a deeper understanding that can provide a solid starting point for other disciplines as well, such as linguistics or psychology. Thus, the sentiment analysis, supplemented with information extraction and named entity recognition analyses, can provide a supported and deeply explored picture of specific sentiment categories and user attitudes.László NemesAttila KissMDPI AGarticlesentiment analysisrecurrent neural networkinformation extractionnamed entity recognitionsocial mediaTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 11017, p 11017 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sentiment analysis recurrent neural network information extraction named entity recognition social media Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	sentiment analysis recurrent neural network information extraction named entity recognition social media Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 László Nemes Attila Kiss Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic
description	Social media platforms are increasingly being used to communicate information, something which has only intensified during the pandemic. News portals and governments are also increasing attention to digital communications, announcements and response or reaction monitoring. Twitter, as one of the largest social networking sites, which has become even more important in the communication of information during the pandemic, provides space for a lot of different opinions and news, with many discussions as well. In this paper, we look at the sentiments of people and we use tweets to determine how people have related to COVID-19 over a given period of time. These sentiment analyses are augmented with information extraction and named entity recognition to get an even more comprehensive picture. The sentiment analysis is based on the ’Bidirectional encoder representations from transformers’ (BERT) model, which is the basic measurement model for the comparisons. We consider BERT as the baseline and compare the results with the RNN, NLTK and TextBlob sentiment analyses. The RNN results are significantly closer to the benchmark results given by BERT, both models are able to categorize all tweets without a single tweet fall into the neutral category. Then, via a deeper analysis of these results, we can get an even more concise picture of people’s emotional state in the given period of time. The data from these analyses further support the emotional categories, and provide a deeper understanding that can provide a solid starting point for other disciplines as well, such as linguistics or psychology. Thus, the sentiment analysis, supplemented with information extraction and named entity recognition analyses, can provide a supported and deeply explored picture of specific sentiment categories and user attitudes.
format	article
author	László Nemes Attila Kiss
author_facet	László Nemes Attila Kiss
author_sort	László Nemes
title	Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic
title_short	Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic
title_full	Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic
title_fullStr	Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic
title_full_unstemmed	Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic
title_sort	information extraction and named entity recognition supported social media sentiment analysis during the covid-19 pandemic
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/34f58199aaf946c1a034c324cb304ede
work_keys_str_mv	AT laszlonemes informationextractionandnamedentityrecognitionsupportedsocialmediasentimentanalysisduringthecovid19pandemic AT attilakiss informationextractionandnamedentityrecognitionsupportedsocialmediasentimentanalysisduringthecovid19pandemic
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Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic

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