COVID-19 Pandemic and Its Effects on Youth Mental Health in Bangladesh

Haque, Mehjabin; Haque, Israt Eshita; Ziku, Md. Noor-e Alam; Ahamed, Naim; Hossain, Md. Sabbir

doi:10.47405/mjssh.v6i10.1071

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…These countries were forced to shut down again, which received much criticism from their citizens. While favored by others, some segments of the population opposed lockdowns due to specific issues, such as unemployment, frustration, and depression [21], [22]. Consequently, many people retreated to online platforms, such as Twitter, Reddit and Facebook, to voice out their feelings and opinions about the continuous implementation of lockdown measures [23].…”

Section: Sars-cov-2 Mostly Known As Covid-19mentioning

confidence: 99%

Public Sentiment Analysis and Topic Modeling Regarding COVID-19’s Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia

2022

KSII TIIS

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The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown 2170 Alamoodi et al.: Public Sentiment Analysis and Topic Modeling Regarding COVID-19's Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysiaas reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.

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Section: Sars-cov-2 Mostly Known As Covid-19mentioning

confidence: 99%

Public Sentiment Analysis and Topic Modeling Regarding COVID-19’s Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia

2022

KSII TIIS

View full text Add to dashboard Cite

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“…Given the importance of public sentiment and the corresponding discussions related to lockdown measures, understanding the COVID-19-related discussions during the quarantine and recovery periods has become critical because the discussions across different periods may significantly vary. Public opinion about specific issues is influenced by the mental status of individuals, which, especially during the lockdown, can be influenced by frustration and depression [9]. These opinions may even shift during relaxed times even if the topic has not been changed.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Approaches for Detecting COVID-19-Lockdown-Related Discussions During Recovery and Lockdown Periods

Baker,

Alamoodi,

Albahri

et al. 2023

J. Oper. Intell.

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Ever since COVID-19 was declared a pandemic, governments around the world have implemented numerous phases of lockdown measures to curb the spread of the virus. These lockdown tactics manifest themselves in the form of widespread fear and panic driven by social media discussions. Given that individuals hold diverse opinions about these lockdown measures during and after their completion, positive and negative lockdown-related discussions should be differentiated to further understand the major related issues and to make appropriate messaging and policy choices in the future. We conduct a sentiment analysis (SA) of COVID-19-lockdown-related tweets by using different machine learning (ML) classifiers and then evaluate their performance before and after using the synthetic minority oversampling technique (SMOTE). This research is performed in five phases, starting with data collection and followed by pre-processing the dataset, preparing the dataset by annotation, applying SMOTE and using ML classifiers. We observe an improvement in accuracy ( ) as confirmed by the Matthew correlation coefficient ( ) across most classifiers, except for the k-nearest neighbour (KNN), whose Acc decreased from 0.82 to 0.59 and MCC decreased from 0.544 to 0.279 before and after SMOTE was applied. Despite the potential of SMOTE with some classifiers, this technique cannot be considered an ultimate solution, especially with other classifiers and datasets. The study provides insights into the need to evaluate and benchmark the integration of data balancing approaches with ML classifiers in addition to considering additional metrics, such as MCC, for binary classification problems, especially in SA.

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COVID-19 Pandemic and Its Effects on Youth Mental Health in Bangladesh

Cited by 2 publications

References 8 publications

Public Sentiment Analysis and Topic Modeling Regarding COVID-19’s Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia

Public Sentiment Analysis and Topic Modeling Regarding COVID-19’s Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia

Comparison of Machine Learning Approaches for Detecting COVID-19-Lockdown-Related Discussions During Recovery and Lockdown Periods

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