Measuring the Impact of Language Models in Sentiment Analysis for Mexico’s COVID-19 Pandemic
The world has been facing the COVID-19 pandemic, which has come with an unprecedented impact on general physical health and financial and social repercussions. The adopted mitigation measures also present significant challenges to the population’s mental health and health-related programs. It is com...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/16/2483 |
| _version_ | 1797410563659137024 |
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| author | Edgar León-Sandoval Mahdi Zareei Liliana Ibeth Barbosa-Santillán Luis Eduardo Falcón Morales |
| author_facet | Edgar León-Sandoval Mahdi Zareei Liliana Ibeth Barbosa-Santillán Luis Eduardo Falcón Morales |
| author_sort | Edgar León-Sandoval |
| collection | DOAJ |
| description | The world has been facing the COVID-19 pandemic, which has come with an unprecedented impact on general physical health and financial and social repercussions. The adopted mitigation measures also present significant challenges to the population’s mental health and health-related programs. It is complex for public organizations to measure the population’s mental health to incorporate its feedback into their decision-making process. A significant portion of the population has turned to social media to express the details of their daily life, making these public data a rich field for understanding emotional and mental well-being. To this end, by using open sentiment analysis tools, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies. Several modern language models were evaluated and compared using intrinsic and extrinsic tasks, that is, the sentiment analysis evaluation of public domain tweets related to the COVID-19 pandemic in Mexico. This study provides a fair evaluation of state-of-the-art language models, such as BERT and VADER, showcasing their metrics and comparing their performance against a real-world task. Results show the importance of selecting the correct language model for large projects such as this one, for there is a need to balance costs with the model’s performance. |
| first_indexed | 2024-03-09T04:31:51Z |
| format | Article |
| id | doaj.art-78b403bf31334643b8ebacd413d2a51c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T04:31:51Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-78b403bf31334643b8ebacd413d2a51c2023-12-03T13:34:04ZengMDPI AGElectronics2079-92922022-08-011116248310.3390/electronics11162483Measuring the Impact of Language Models in Sentiment Analysis for Mexico’s COVID-19 PandemicEdgar León-Sandoval0Mahdi Zareei1Liliana Ibeth Barbosa-Santillán2Luis Eduardo Falcón Morales3School of Engineering and Sciences, Tecnologico de Monterrey, Zapopan 45201, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Zapopan 45201, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Zapopan 45201, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Zapopan 45201, MexicoThe world has been facing the COVID-19 pandemic, which has come with an unprecedented impact on general physical health and financial and social repercussions. The adopted mitigation measures also present significant challenges to the population’s mental health and health-related programs. It is complex for public organizations to measure the population’s mental health to incorporate its feedback into their decision-making process. A significant portion of the population has turned to social media to express the details of their daily life, making these public data a rich field for understanding emotional and mental well-being. To this end, by using open sentiment analysis tools, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies. Several modern language models were evaluated and compared using intrinsic and extrinsic tasks, that is, the sentiment analysis evaluation of public domain tweets related to the COVID-19 pandemic in Mexico. This study provides a fair evaluation of state-of-the-art language models, such as BERT and VADER, showcasing their metrics and comparing their performance against a real-world task. Results show the importance of selecting the correct language model for large projects such as this one, for there is a need to balance costs with the model’s performance.https://www.mdpi.com/2079-9292/11/16/2483sentiment analysislanguage model evaluationbig dataCOVID-19machine learningMexico |
| spellingShingle | Edgar León-Sandoval Mahdi Zareei Liliana Ibeth Barbosa-Santillán Luis Eduardo Falcón Morales Measuring the Impact of Language Models in Sentiment Analysis for Mexico’s COVID-19 Pandemic Electronics sentiment analysis language model evaluation big data COVID-19 machine learning Mexico |
| title | Measuring the Impact of Language Models in Sentiment Analysis for Mexico’s COVID-19 Pandemic |
| title_full | Measuring the Impact of Language Models in Sentiment Analysis for Mexico’s COVID-19 Pandemic |
| title_fullStr | Measuring the Impact of Language Models in Sentiment Analysis for Mexico’s COVID-19 Pandemic |
| title_full_unstemmed | Measuring the Impact of Language Models in Sentiment Analysis for Mexico’s COVID-19 Pandemic |
| title_short | Measuring the Impact of Language Models in Sentiment Analysis for Mexico’s COVID-19 Pandemic |
| title_sort | measuring the impact of language models in sentiment analysis for mexico s covid 19 pandemic |
| topic | sentiment analysis language model evaluation big data COVID-19 machine learning Mexico |
| url | https://www.mdpi.com/2079-9292/11/16/2483 |
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