A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic

Hugo Moises Montesinos-Yufa; Emily Musgrove

doi:doi:10.11648/j.ijdst.20241002.13

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A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic

Hugo Moises Montesinos-Yufa^*

, Emily Musgrove

Published in International Journal on Data Science and Technology (Volume 10, Issue 2)

Received: 4 July 2024 Accepted: 1 August 2024 Published: 27 August 2024

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Abstract

This study investigates the impact of the COVID-19 pandemic on the connotative language used in news articles, leveraging sentiment analysis to gauge shifts in societal attitudes and potential implications for mental health. Utilizing the statistical programming language R, we extracted and analyzed texts from 645 articles published before and during the pandemic by nine authors across three major U.S. newspapers: The Wall Street Journal, New York Times, and The Washington Post. Employing the AFINN and NRC sentiment lexicons, we observed a statistically significant decrease in sentiment during the pandemic period (p < 0.0001), suggesting a pervasive shift in media discourse. This decline, consistent across newspapers and journalists, highlights the profound impact of the pandemic on societal attitudes, reflecting the pain and stress experienced by many. Such a decline in sentiment can create a negative feedback loop that exacerbates the already significant health and behavioral challenges triggered by the pandemic and its associated mitigation measures. Our findings underscore the value of sentiment analysis and text mining in assessing the effects of high-stress, long-term events on global public health while identifying a gap in the existing literature that prioritizes disease-focused research over holistic well-being. This study highlights the critical role of journalists and leaders in shaping public sentiment during crises, advocating for early recognition of concerning trends. It also offers a valuable framework for future research connecting major events with the overall media sentiment and their subsequent effects on public health.

Published in	International Journal on Data Science and Technology (Volume 10, Issue 2)
DOI	10.11648/j.ijdst.20241002.13
Page(s)	38-44
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

COVID-19, Sentiment Analysis, Mental Health, News Media, Text Mining, Pandemics, Social Attitudes, Linguistics

References

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APA Style

Montesinos-Yufa, H. M., Musgrove, E. (2024). A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic. International Journal on Data Science and Technology, 10(2), 38-44. https://doi.org/10.11648/j.ijdst.20241002.13

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ACS Style

Montesinos-Yufa, H. M.; Musgrove, E. A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic. Int. J. Data Sci. Technol. 2024, 10(2), 38-44. doi: 10.11648/j.ijdst.20241002.13

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AMA Style

Montesinos-Yufa HM, Musgrove E. A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic. Int J Data Sci Technol. 2024;10(2):38-44. doi: 10.11648/j.ijdst.20241002.13

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@article{10.11648/j.ijdst.20241002.13,
  author = {Hugo Moises Montesinos-Yufa and Emily Musgrove},
  title = {A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic
},
  journal = {International Journal on Data Science and Technology},
  volume = {10},
  number = {2},
  pages = {38-44},
  doi = {10.11648/j.ijdst.20241002.13},
  url = {https://doi.org/10.11648/j.ijdst.20241002.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20241002.13},
  abstract = {This study investigates the impact of the COVID-19 pandemic on the connotative language used in news articles, leveraging sentiment analysis to gauge shifts in societal attitudes and potential implications for mental health. Utilizing the statistical programming language R, we extracted and analyzed texts from 645 articles published before and during the pandemic by nine authors across three major U.S. newspapers: The Wall Street Journal, New York Times, and The Washington Post. Employing the AFINN and NRC sentiment lexicons, we observed a statistically significant decrease in sentiment during the pandemic period (p < 0.0001), suggesting a pervasive shift in media discourse. This decline, consistent across newspapers and journalists, highlights the profound impact of the pandemic on societal attitudes, reflecting the pain and stress experienced by many. Such a decline in sentiment can create a negative feedback loop that exacerbates the already significant health and behavioral challenges triggered by the pandemic and its associated mitigation measures. Our findings underscore the value of sentiment analysis and text mining in assessing the effects of high-stress, long-term events on global public health while identifying a gap in the existing literature that prioritizes disease-focused research over holistic well-being. This study highlights the critical role of journalists and leaders in shaping public sentiment during crises, advocating for early recognition of concerning trends. It also offers a valuable framework for future research connecting major events with the overall media sentiment and their subsequent effects on public health.
},
 year = {2024}
}

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TY  - JOUR
T1  - A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic

AU  - Hugo Moises Montesinos-Yufa
AU  - Emily Musgrove
Y1  - 2024/08/27
PY  - 2024
N1  - https://doi.org/10.11648/j.ijdst.20241002.13
DO  - 10.11648/j.ijdst.20241002.13
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 38
EP  - 44
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20241002.13
AB  - This study investigates the impact of the COVID-19 pandemic on the connotative language used in news articles, leveraging sentiment analysis to gauge shifts in societal attitudes and potential implications for mental health. Utilizing the statistical programming language R, we extracted and analyzed texts from 645 articles published before and during the pandemic by nine authors across three major U.S. newspapers: The Wall Street Journal, New York Times, and The Washington Post. Employing the AFINN and NRC sentiment lexicons, we observed a statistically significant decrease in sentiment during the pandemic period (p < 0.0001), suggesting a pervasive shift in media discourse. This decline, consistent across newspapers and journalists, highlights the profound impact of the pandemic on societal attitudes, reflecting the pain and stress experienced by many. Such a decline in sentiment can create a negative feedback loop that exacerbates the already significant health and behavioral challenges triggered by the pandemic and its associated mitigation measures. Our findings underscore the value of sentiment analysis and text mining in assessing the effects of high-stress, long-term events on global public health while identifying a gap in the existing literature that prioritizes disease-focused research over holistic well-being. This study highlights the critical role of journalists and leaders in shaping public sentiment during crises, advocating for early recognition of concerning trends. It also offers a valuable framework for future research connecting major events with the overall media sentiment and their subsequent effects on public health.

VL  - 10
IS  - 2
ER  -

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Author Information

Hugo Moises Montesinos-Yufa

Department of Mathematics, Computer Science, and Statistics, Ursinus College, Collegeville, United States

Contact Email

http://orcid.org/0000-0001-6957-7519
Emily Musgrove

Department of Mathematics, Computer Science, and Statistics, Ursinus College, Collegeville, United States

Contact Email

http://orcid.org/0009-0009-8695-3489

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APA Style

Montesinos-Yufa, H. M., Musgrove, E. (2024). A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic. International Journal on Data Science and Technology, 10(2), 38-44. https://doi.org/10.11648/j.ijdst.20241002.13

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ACS Style

Montesinos-Yufa, H. M.; Musgrove, E. A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic. Int. J. Data Sci. Technol. 2024, 10(2), 38-44. doi: 10.11648/j.ijdst.20241002.13

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AMA Style

Montesinos-Yufa HM, Musgrove E. A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic. Int J Data Sci Technol. 2024;10(2):38-44. doi: 10.11648/j.ijdst.20241002.13

Copy | Download

@article{10.11648/j.ijdst.20241002.13,
  author = {Hugo Moises Montesinos-Yufa and Emily Musgrove},
  title = {A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic
},
  journal = {International Journal on Data Science and Technology},
  volume = {10},
  number = {2},
  pages = {38-44},
  doi = {10.11648/j.ijdst.20241002.13},
  url = {https://doi.org/10.11648/j.ijdst.20241002.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20241002.13},
  abstract = {This study investigates the impact of the COVID-19 pandemic on the connotative language used in news articles, leveraging sentiment analysis to gauge shifts in societal attitudes and potential implications for mental health. Utilizing the statistical programming language R, we extracted and analyzed texts from 645 articles published before and during the pandemic by nine authors across three major U.S. newspapers: The Wall Street Journal, New York Times, and The Washington Post. Employing the AFINN and NRC sentiment lexicons, we observed a statistically significant decrease in sentiment during the pandemic period (p < 0.0001), suggesting a pervasive shift in media discourse. This decline, consistent across newspapers and journalists, highlights the profound impact of the pandemic on societal attitudes, reflecting the pain and stress experienced by many. Such a decline in sentiment can create a negative feedback loop that exacerbates the already significant health and behavioral challenges triggered by the pandemic and its associated mitigation measures. Our findings underscore the value of sentiment analysis and text mining in assessing the effects of high-stress, long-term events on global public health while identifying a gap in the existing literature that prioritizes disease-focused research over holistic well-being. This study highlights the critical role of journalists and leaders in shaping public sentiment during crises, advocating for early recognition of concerning trends. It also offers a valuable framework for future research connecting major events with the overall media sentiment and their subsequent effects on public health.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - A Sentiment Analysis of News Articles Published Before and During the COVID-19 Pandemic

AU  - Hugo Moises Montesinos-Yufa
AU  - Emily Musgrove
Y1  - 2024/08/27
PY  - 2024
N1  - https://doi.org/10.11648/j.ijdst.20241002.13
DO  - 10.11648/j.ijdst.20241002.13
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 38
EP  - 44
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20241002.13
AB  - This study investigates the impact of the COVID-19 pandemic on the connotative language used in news articles, leveraging sentiment analysis to gauge shifts in societal attitudes and potential implications for mental health. Utilizing the statistical programming language R, we extracted and analyzed texts from 645 articles published before and during the pandemic by nine authors across three major U.S. newspapers: The Wall Street Journal, New York Times, and The Washington Post. Employing the AFINN and NRC sentiment lexicons, we observed a statistically significant decrease in sentiment during the pandemic period (p < 0.0001), suggesting a pervasive shift in media discourse. This decline, consistent across newspapers and journalists, highlights the profound impact of the pandemic on societal attitudes, reflecting the pain and stress experienced by many. Such a decline in sentiment can create a negative feedback loop that exacerbates the already significant health and behavioral challenges triggered by the pandemic and its associated mitigation measures. Our findings underscore the value of sentiment analysis and text mining in assessing the effects of high-stress, long-term events on global public health while identifying a gap in the existing literature that prioritizes disease-focused research over holistic well-being. This study highlights the critical role of journalists and leaders in shaping public sentiment during crises, advocating for early recognition of concerning trends. It also offers a valuable framework for future research connecting major events with the overall media sentiment and their subsequent effects on public health.

VL  - 10
IS  - 2
ER  -

Copy | Download