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

Author

Alamoodi, A. H., Baker, Mohammed Rashad, Albahri, O. S., Zaidan, B. B., Zaidan, A. A., Wing-Kwong Wong, Garfan, Salem, Albahri, A. S., Alonso, Miguel A., Jasim, Ali Najm, and Baqer, M. J.

Subjects
PUBLIC opinion, SENTIMENT analysis, STAY-at-home orders, QUARANTINE, COVID-19, COVID-19 pandemic
Abstract

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 as 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. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation.

Author

Alamoodi, A.H., Zaidan, B.B., Zaidan, A.A., Albahri, O.S., Chen, Juliana, Chyad, M.A., Garfan, Salem, and Aleesa, A.M.

Subjects
*SOFT computing, *MISSING data (Statistics), *DATA distribution, *CAPABILITIES approach (Social sciences), *CHILD development
Abstract

• A soft computing approach to address non-reference missing data at large volumes. • An approach to reconstruct dataset with huge amount of missing. • Making a representative portions of data for missing data imputation. Missing data is a common problem in real-world data sets and it is amongst the most complex topics in computer science and many other research domains. The common ways to cope with missing values are either by elimination or imputation depending of the volume of the missing data and its distribution nature. It becomes imperative to come up with new imputation approaches along with efficient algorithms. Though most existing imputation methods focus on a moderate amount of missing data, imputation for high missing rates over 80% is still important but challenging. Even with the existence of some works in addressing high missing volume issue, they mostly rely on imputing reference dataset (Complete Datasets for evaluation) after they create artificial missing values and impute it to measure the accuracy of their proposed techniques. So far, the option of imputing high proportions of missing values with no reference comparison dataset (Original Dataset with highly missing values) have been often ignored or overlooked. Therefore, we propose a missing data imputation approach for high volumes of missing values with no reference comparison dataset. The approach makes use of pre-processing measures and breaking the dataset into small continuous non-missing portions then using Multi Criteria Decision-making analysis to select a portion of data which is representative of the entire broken datasets. This portion helps to create reference comparisons and expands the missing dataset through artificial missing-making procedures with different percentages and imputation using different machine learning techniques. This study conducted two experiments using BMI datasets with more than 80% of missing values, derived from the National Child Development Centre (NCDRC) at Sultan Idris Education University (UPSI), Malaysia. The results show that our approach capability in reconstructing datasets with huge missing values. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
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