Cargando…
Sentiment analysis of COVID-19 social media data through machine learning
Pandemics are a severe threat to lives in the universe and our universe encounters several pandemics till now. COVID-19 is one of them, which is a viral infectious disease that increased morbidity and mortality worldwide. This has a negative impact on countries’ economies, as well as social and poli...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9309239/ https://www.ncbi.nlm.nih.gov/pubmed/35912062 http://dx.doi.org/10.1007/s11042-022-13492-w |
_version_ | 1784753113564971008 |
---|---|
author | Dangi, Dharmendra Dixit, Dheeraj K. Bhagat, Amit |
author_facet | Dangi, Dharmendra Dixit, Dheeraj K. Bhagat, Amit |
author_sort | Dangi, Dharmendra |
collection | PubMed |
description | Pandemics are a severe threat to lives in the universe and our universe encounters several pandemics till now. COVID-19 is one of them, which is a viral infectious disease that increased morbidity and mortality worldwide. This has a negative impact on countries’ economies, as well as social and political concerns throughout the world. The growths of social media have witnessed much pandemic-related news and are shared by many groups of people. This social media news was also helpful to analyze the effects of the pandemic clearly. Twitter is one of the social media networks where people shared COVID-19 related news in a wider range. Meanwhile, several approaches have been proposed to analyze the COVID-19 related sentimental analysis. To enhance the accuracy of sentimental analysis, we have proposed a novel approach known as Sentimental Analysis of Twitter social media Data (SATD). Our proposed method is based on five different machine learning models such as Logistic Regression, Random Forest Classifier, Multinomial NB Classifier, Support Vector Machine, and Decision Tree Classifier. These five classifiers possess various advantages and hence the proposed approach effectively classifies the tweets from the Twint. Experimental analyses are made and these classifier models are used to calculate different values such as precision, recall, f1-score, and support. Moreover, the results are also represented as a confusion matrix, accuracy, precision, and receiver operating characteristic (ROC) graphs. From the experimental and discussion section, it is obtained that the accuracy of our proposed classifier model is high. |
format | Online Article Text |
id | pubmed-9309239 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-93092392022-07-25 Sentiment analysis of COVID-19 social media data through machine learning Dangi, Dharmendra Dixit, Dheeraj K. Bhagat, Amit Multimed Tools Appl 1220: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System Pandemics are a severe threat to lives in the universe and our universe encounters several pandemics till now. COVID-19 is one of them, which is a viral infectious disease that increased morbidity and mortality worldwide. This has a negative impact on countries’ economies, as well as social and political concerns throughout the world. The growths of social media have witnessed much pandemic-related news and are shared by many groups of people. This social media news was also helpful to analyze the effects of the pandemic clearly. Twitter is one of the social media networks where people shared COVID-19 related news in a wider range. Meanwhile, several approaches have been proposed to analyze the COVID-19 related sentimental analysis. To enhance the accuracy of sentimental analysis, we have proposed a novel approach known as Sentimental Analysis of Twitter social media Data (SATD). Our proposed method is based on five different machine learning models such as Logistic Regression, Random Forest Classifier, Multinomial NB Classifier, Support Vector Machine, and Decision Tree Classifier. These five classifiers possess various advantages and hence the proposed approach effectively classifies the tweets from the Twint. Experimental analyses are made and these classifier models are used to calculate different values such as precision, recall, f1-score, and support. Moreover, the results are also represented as a confusion matrix, accuracy, precision, and receiver operating characteristic (ROC) graphs. From the experimental and discussion section, it is obtained that the accuracy of our proposed classifier model is high. Springer US 2022-07-25 2022 /pmc/articles/PMC9309239/ /pubmed/35912062 http://dx.doi.org/10.1007/s11042-022-13492-w Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | 1220: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System Dangi, Dharmendra Dixit, Dheeraj K. Bhagat, Amit Sentiment analysis of COVID-19 social media data through machine learning |
title | Sentiment analysis of COVID-19 social media data through machine learning |
title_full | Sentiment analysis of COVID-19 social media data through machine learning |
title_fullStr | Sentiment analysis of COVID-19 social media data through machine learning |
title_full_unstemmed | Sentiment analysis of COVID-19 social media data through machine learning |
title_short | Sentiment analysis of COVID-19 social media data through machine learning |
title_sort | sentiment analysis of covid-19 social media data through machine learning |
topic | 1220: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9309239/ https://www.ncbi.nlm.nih.gov/pubmed/35912062 http://dx.doi.org/10.1007/s11042-022-13492-w |
work_keys_str_mv | AT dangidharmendra sentimentanalysisofcovid19socialmediadatathroughmachinelearning AT dixitdheerajk sentimentanalysisofcovid19socialmediadatathroughmachinelearning AT bhagatamit sentimentanalysisofcovid19socialmediadatathroughmachinelearning |