Sentiment Analysis on COVID-19 Vaccination in Bangladesh

The COVID-19 pandemic has caused a shocking loss of life on a worldwide scale and influenced every sector of Bangladesh very badly. The simplest method for preventing infectious diseases is vaccination. Bangladeshi netizens discuss their opinions, feelings, and experiences associated with the COVID-19 vaccination program on social media platforms. The purpose of this research is to conduct a sentiment analysis of the vaccination campaign, and for this purpose, the reactions of Bangladeshi netizens on social media to the vaccination program were collected. The dataset was manually labelled into two categories: positive and negative. Then process the dataset using Natural Language Processing (NLP). The processed data is then classified using various machine learning algorithms using N-gram as a feature extraction method. The recall, precision, f1-score, and accuracy of various algorithms are all measured. The experiment results show that 61% of the reviews indicate the positive aspects of the vaccination program, while 39% are negative. For unigram, bigram, and trigram, the very best accuracy was achieved by Logistic Regression (LR) at 80.70%, 79.45%, and 78.65%. © 2022 IEEE.

Sentiment Analysis of COVID-19 Vaccination in Bangla Language with Code-Mixed Text from Social Media

The vaccination program which helps avert pandemics is facing new hurdles, including the emergence of hazardous new virus strains and public distrust. Analyzing the sentiment expressed in social media interactions related to vaccines may aid the health authority in implementing public safety procedures and guide the government in developing appropriate policies. The purpose of this research is to identify the public sentiments toward the COVID-19 vaccination in Bangladesh from social media comments. Comments posted on social media platforms often mix formal and informal language known as code-mixed text and do not adhere to any particular grammatical standards. In addition, the Bangla language lacks computational models and annotated resources for sentiment analysis. To overcome this, we created CoVaxBD, a Bangla-English code-mixed and sentiment-annotated corpus of Facebook comments. This paper also proposes a model for sentiment analysis based on the multilingual BERT. It achieves a validation accuracy of around 97.3 % and a precision score of approximately 97.4%. © 2022 IEEE.

Psychometric Validation of the Bangla Fear of COVID-19 Scale: Confirmatory Factor Analysis and Rasch Analysis.

The recently developed Fear of COVID-19 Scale (FCV-19S) is a seven-item uni-dimensional scale that assesses the severity of fears of COVID-19. Given the rapid increase of COVID-19 cases in Bangladesh, we aimed to translate and validate the FCV-19S in Bangla. The forward-backward translation method was used to translate the English version of the questionnaire into Bangla. The reliability and validity properties of the Bangla FCV-19S were rigorously psychometrically evaluated (utilizing both confirmatory factor analysis and Rasch analysis) in relation to socio-demographic variables, national lockdown variables, and response to the Bangla Health Patient Questionnaire. The sample comprised 8550 Bangladeshi participants. The Cronbach α value for the Bangla FCV-19S was 0.871 indicating very good internal reliability. The results of the confirmatory factor analysis showed that the uni-dimensional factor structure of the FCV-19S fitted well with the data. The FCV-19S was significantly correlated with the nine-item Bangla Patient Health Questionnaire (PHQ-90) (r = 0.406, p < 0.001). FCV-19S scores were significantly associated with higher worries concerning lockdown. Measurement invariance of the FCV-19S showed no differences with respect to age or gender. The Bangla version of FCV-19S is a valid and reliable tool with robust psychometric properties which will be useful for researchers carrying out studies among the Bangla speaking population in assessing the psychological impact of fear from COVID-19 infection during this pandemic.

Comparison of Different Sentiment Analysis Techniques for Bangla Reviews

Internet adoption has increased rapidly during the worldwide COVID-19 pandemic. Nowadays people not only prefer to shop using various e-commerce platforms, but also like to provide feedback and express their opinions and experiences using the online platforms. Since new customers try to understand the products' utility and acceptability from other consumers' reviews, it has become crucial to analyze the customers' sentiments and opinions on each product. In this paper, we have presented a sentiment analysis technique on the basis of product reviews written in Bangla language to better understand the combined consumer perspective. Our work aims to compare existing classifiers' performance and find the best algorithm for our dataset. We collected reviews from the leading Bangla bookselling e-commerce site 'Rokomari.com' for this work. We implemented ML and DL classifier models and compared their overall performance on this dataset. The experimental studies show that the best accuracy is achieved from LSTM and SGD over the other implemented ML and DL based classifier models. © 2022 IEEE.

The psychometric properties of the Bangla Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): preliminary reports from a large-scale validation study

Background The Posttraumatic Stress Disorder Checklist (PCL-5) is the most widely used screening tool in assessing posttraumatic stress disorder symptoms, based on the Diagnostic and Statistical Manual of Mental disorders (DSM-5) criteria. This study aimed to evaluate the psychometric properties of the newly translated Bangla PCL-5. Methods A cross-sectional survey was carried out among 10,605 individuals (61.0% male;mean age: 23.6 ± 5.5 [13–71 years]) during May and June 2020, several months after the onset of the COVID-19 outbreak in Bangladesh. The survey included the Bangla PCL-5 and the PHQ-9 depression scale. We used confirmatory factor analysis to test the four-factor DSM-5 model, the six-factor Anhedonia model, and the seven-factor hybrid model. Results The Bangla PCL-5 displayed adequate internal consistency (Cronbach’s alpha = 0.90). The Bangla PCL-5 score was significantly correlated with scores of the PHQ-9 depression scale, confirming strong convergent validity. Confirmatory factor analyses indicated the models had a good fit to the data, including the four-factor DSM-5 model, the six-factor Anhedonia model, and the seven-factor hybrid model. Overall, the seven-factor hybrid model exhibited the best fit to the data. Conclusions The Bangla PCL-5 appears to be a valid and reliable psychometric screening tool that may be employed in the prospective evaluation of posttraumatic stress disorder in Bangladesh.

The psychometric properties of the Bangla Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): preliminary reports from a large-scale validation study.

BACKGROUND: The Posttraumatic Stress Disorder Checklist (PCL-5) is the most widely used screening tool in assessing posttraumatic stress disorder symptoms, based on the Diagnostic and Statistical Manual of Mental disorders (DSM-5) criteria. This study aimed to evaluate the psychometric properties of the newly translated Bangla PCL-5. METHODS: A cross-sectional survey was carried out among 10,605 individuals (61.0% male; mean age: 23.6 ± 5.5 [13-71 years]) during May and June 2020, several months after the onset of the COVID-19 outbreak in Bangladesh. The survey included the Bangla PCL-5 and the PHQ-9 depression scale. We used confirmatory factor analysis to test the four-factor DSM-5 model, the six-factor Anhedonia model, and the seven-factor hybrid model. RESULTS: The Bangla PCL-5 displayed adequate internal consistency (Cronbach's alpha = 0.90). The Bangla PCL-5 score was significantly correlated with scores of the PHQ-9 depression scale, confirming strong convergent validity. Confirmatory factor analyses indicated the models had a good fit to the data, including the four-factor DSM-5 model, the six-factor Anhedonia model, and the seven-factor hybrid model. Overall, the seven-factor hybrid model exhibited the best fit to the data. CONCLUSIONS: The Bangla PCL-5 appears to be a valid and reliable psychometric screening tool that may be employed in the prospective evaluation of posttraumatic stress disorder in Bangladesh.

Promoting Mental Health and Wellbeing in Multicultural Australia: A Collaborative Regional Approach.

Migrant communities are often under-served by mental health services. Lack of community engagement results in missed opportunities for mental health promotion and early intervention, delayed care, and high rates of untreated psychological distress. Bilingual clinicians and others who work with these communities lack linguistically and culturally appropriate resources. This article reports on the implementation and evaluation of a community-based group mindfulness program delivered to Arabic and Bangla-speaking communities in Sydney, Australia, including modifications made to the content and format in response to the COVID-19 pandemic. The program was positioned within a stepped-care model for primary mental health care and adopted a collaborative regional approach. In addition to improved mental health outcomes for face-to-face and online program participants, we have documented numerous referrals to specialist services and extensive diffusion of mindfulness skills, mostly to family members, within each community. Community partnerships were critical to community engagement. Training workshops to build the skills of the bilingual health and community workforce increased the program's reach. In immigrant nations such as Australia, mainstream mental health promotion must be complemented by activities that target specific population groups. Scaled up, and with appropriate adaptation, the group mindfulness program offers a low-intensity in-language intervention for under-served communities.

Emotion Detection Based on Bangladeshi People's Social Media Response on COVID-19.

The COVID-19 pandemic is still active on a global scale while the virus was first identified in December 2019 in Wuhan, China. As the pandemic continues to affect millions of lives, several countries including Bangladesh have gone into complete lockdown for a second time. During the lockdown periods, people have expressed their experiences, curiosities, and ideas regarding the problems caused by the pandemic in terms of health and socioeconomic issues. This study was conducted to determine how Bangladeshi people are responding to and dealing with the circumstances of COVID-19. This study took into account the status and comments on those issues related to COVID-19 from a variety of Facebook pages and YouTube channels run by reputable Bangladeshi news organizations and health experts. Throughout the study, several machine learning methods were studied, ranging from classical algorithms which include SVM and Random Forest, while CNN and LSTM are the deep learning algorithms to conduct experiments on a classified data set that belongs to the authors, which contains 10,581 data points. While evaluating the efficiency of these models in terms of model assessment, the finding suggests that LSTM outperforms all others with an accuracy of 84.92.