ADAPTATION OF DOMAIN-SPECIFIC TRANSFORMER MODELS WITH TEXT OVERSAMPLING FOR SENTIMENT ANALYSIS OF SOCIAL MEDIA POSTS ON COVID-19 VACCINE

Covid-19 has spread across the world, and several vaccines have been developed to counter its surge. To identify the correct sentiments that are associated with the vaccines from social media posts, we fine-tune various state-of-the-art pretrained transformer models on tweets that are associated with Covid-19 vaccines. Specifically, we use the recently introduced state-of-the-art RoBERTa, XLNet, and BERT pre-trained transformer models, and the domain-specific CT-BERT and BERTweet transformer models that have been pre-trained on Covid-19 tweets. We further explore the option of text augmentation by oversampling using the language model-based oversampling technique (LMOTE) to improve the accuracies of these models – specifically, for small sample data sets where there is an imbalanced class distribution among the positive, negative, and neutral sentiment classes. Our results summarize our findings on the suitability of text oversampling for imbalanced small-sample data sets that are used to fine-tune state-of-the-art pre-trained transformer models as well as the utility of domain-specific transformer models for the classification task. © 2023 Author(s). This is an open access publication, which can be used, distributed and reproduced in any medium according to the Creative Commons CC-BY 4.0 License.

Dynamic Topic Modeling of Covid-19 Vaccine-Related Tweets

The Covid-19 pandemic has made a huge impact on the world. Vaccines are regarded as the universal solution to mitigate the spread of the pandemic. Vaccination programs have been initiated by all countries in the past one year or so. The public opinion about vaccinations has been dynamically changing during this period. We intend to track the perception of the masses since the arrival of the vaccines, through social media posts, and reflect on the reasons behind the dynamically evolving ideas of people. For this purpose, we propose the use of Latent Dirichlet Allocation (LDA) for topic modeling from vaccine-related discussions on the popular social media platform Twitter, in five temporal phases, in the duration of 20 December 2020 to 16 October 2021. The time windows are determined such that the tweets are equally distributed in each time slice. The ten most relevant terms in the top-10 topics in each time window are determined and presented in the form of bar charts. The relevancy of a term is interpreted as the sum of probabilistic scores associated with that term in the top-10 topics identified by LDA in a particular time period. The bar charts are further analyzed for inferring the topics of discussion in a particular phase of time. © 2022 ACM.

Covid-19 Vaccine Sentiment Analysis During Second Wave in India by Transfer Learning Using XLNet

The Covid-19 pandemic has created a world-wide crisis from the perspectives of health and economy. Vaccination is one of the prime means by which herd immunity could be developed. Social media platforms such as Twitter has played a major role in building public opinion as the vaccination drive got underway in several countries. In this paper, we present a tweet-based sentiment analysis of the two popularly administered vaccines in India Covishield and Covaxin during the second wave of the pandemic in India, from March 2021 to September 2021, which was attributed to the Delta mutant of the coronavirus. We use unlabeled Covid-19 vaccine-related tweets downloaded from a large-scale dataset from March 2021 to September 2021, and employ transfer learning for classifying the unlabeled tweets. The contributions of this paper are: - sentiment analysis of unlabeled vaccine-related tweets by training a transformer model on pre-trained XLNet (transformer) features derived from a labeled non-Covid Twitter dataset, a time-line of public sentiments for the two vaccines administered in India, and word clouds of high-frequency adjective unigrams after sentiment analysis, as evidence. © 2022, Springer Nature Switzerland AG.

Significant different Perceptions of the Viral Dynamics and the Immune System to Severe Acute Respiratory Syndrome Coronavirus 2 (SARSCOV- 2) Exposure

Aim: The knowledge of viral characteristics in addition immune reply to severe respiratory disorder (Sars Syndrome Coronavirus 2 (SARS-CoV-2) contamination still has significant gaps. Methods: In a retrospective longitudinal cohort analysis of 140 cases having PCR-established SARS-CoV-2 disease, researchers analyzed those parameters and demonstrated their correlation with symptom manifestations (mean age, 44 years;54 percent male;48 percent through comorbidities). Breathing models (n = 76) remained obtained for viral culture, serum specimens (n = 32) for IgM/IgG levels, and plasma samples (n = 82) for inflammatory cytokines and chemokines. The illness burden remained connected to the findings of viral culture, serologic tests, also immunological markers. Results: Fifty-eight (58%) cases established viral pneumonia, including 22 (18%) requiring supplementary oxygen and 14 (11%) requiring invasive mechanical ventilation. Twenty of the 77 individuals were positive for viral culture from respiratory samples (24 percent). When the PCR cycle threshold (Ct) value remained more than 31 or greater than 15 days following indication onset, no virus was recovered. Seroconversion happened at a median (IQR) of 13.6 (10-20) days for IgM and 16.1 (14-22) days for IgG;56/63 patients (88.2 percent) seroconverted on day 15 or later. Health hazard appeared linked to quicker seroconversion as well as greater peak IgM and IgG levels. Conclusion: Researchers discovered that viral viability significantly related having such a lower PCR Ct charge in the initial stages of disease. The seriousness of the illness was linked to a greater antibody level. Overcharged pro-inflammatory immune markers provide marks for host-directed immunotherapy, that would have been investigated in randomized precise studies.

COVID-19 and Diabetes Mellitus: A Life Threatening Reciprocal Association

COVID-19 storm has taken the world and is now posing a massive burden on the healthcare services of the world. Another long-standing global epidemic is diabetes mellitus and diabetics who get infected with COVID-19 have been seen to have worse outcomes and a high non-survival rate. The global focus is to control the pandemic for which diabetes has been proved to be a vulnerable group. The aim of the present review was to assemble the information about diabetes mellitus and COVID-19 mainly focusing on the interrelation of pandemics of the past and diabetes mellitus, possible pathophysiological mechanisms governing COVID-19 in diabetics, the effect of COVID-19 infection on underlying diabetes mellitus, morbidity, and mortality in diabetic COVID-19 patients, and finally the management of Diabetes Mellitus (DM) in the current pandemic. It was concluded that this COVID-19 pandemic is still lurking and it is of great importance to highlight the fact that a high percentage of the population of the world is affected by various comorbidities like diabetes mellitus, hypertension, COPD, obesity, etc., which makes a subset of the population more vulnerable. This vulnerable population is at increased risk for a poor outcome if affected by COVID-19. Hence, we as a society should prioritize this population at risk to avoid adding additional burden to the already overburdened health care system in the present COVID-19 scenario.

COVID-19 and diabetes mellitus: a life-threatening reciprocal association

COVID-19 has taken the world by storm and is now posing as a massive burden on the healthcare services of the world. Another long-standing global epidemic is Diabetes mellitus and Diabetics who get infected with COVID-19 have been seen to have worse outcomes and a high non-survival rate. The global focus is to control the pandemic for which diabetes has proved to be a vulnerable group. The present review aimed to assemble the information about Diabetes mellitus and COVID-19 mainly focusing on the interrelation of pandemics of the past and Diabetes mellitus, possible pathophysiological mechanisms governing COVID-19 in diabetics, the effect of COVID-19 infection on underlying Diabetes mellitus, morbidity, and mortality in diabetic COVID-19 patients and finally the management of Diabetes mellitus in the current pandemic.it is concluded that this COVID-19 pandemic is still lurking and it is of great importance to highlight the fact that a high percentage of the population of the world is affected by various comorbidities like diabetes mellitus, hypertension, COPD, obesity, etc which makes a subset of the population more vulnerable. This vulnerable population is at increased risk for a poor outcome if affected by COVID-19. Hence, we as a society should prioritize this population at risk to avoid adding additional burden to the already overburdened health care system in the present COVID-19 scenario.

Frequency of confirmed corona infection (covid-19) in children in affected families of gujrat

Aim: To detect the frequency of confirmed corona infection (covid-19) in children in affected families in Gujrat. Study design;cross sectional study. Place and duration of study: This study was conducted from January 2020 to 10 June 2020 at Gujrat Pakistan. Methods: Total 214 children were included in the study from 141 families in which at least one family member was confirmed positive for corona virus infection (COVID-19). This study was started in the beginning of year 2020 but in Gujrat first positive case was reported in March 2020. First of all a family was decided where a confirmed positive case was there. This particular family was included and decided for testing if there was abroad travelling history within 14 days or there was a contact with a confirmed positive patient for corona infection (COVID-19). Results: Total 214 children were included from January 2020 to June 2020. Out of total 214 children, 78 children were positive for (COVID-19) corona infection. Out of 78 (COVID-19) corona infection positive children, 46(58.97%) were female children whereas 32(41.02%) were male children. 37 children (47.44%) were from 1 to 6 years of age whereas 41 (52.56%) were 7-15 years of age. Conclusions: Children can acquire corona virus infection (COVID-19) from adults. Though the severity of corona virus infection (COVID-19) is mild in children, in spite of all this the children should not come in contact with positive person for corona virus infection (COVID-19).

Covid-19 and Diabetes Mellitus: A Life-Threatening Reciprocal Association

COVID-19 has taken the world by storm and is now posing as a massive burden on the healthcare services of the world. Another long-standing global epidemic is Diabetes mellitus and Diabetics who get infected with COVID-19 have been seen to have worse outcomes and a high non-survival rate. The global focus is to control the pandemic for which diabetes has proved to be a vulnerable group. The present review aimed to assemble the information about Diabetes mellitus and COVID-19 mainly focusing on the interrelation of pandemics of the past and Diabetes mellitus, possible pathophysiological mechanisms governing COVID-19 in diabetics, the effect of COVID-19 infection on underlying Diabetes mellitus, morbidity, and mortality in diabetic COVID-19 patients and finally the management of Diabetes mellitus in the current pandemic.it is concluded that this COVID-19 pandemic is still lurking and it is of great importance to highlight the fact that a high percentage of the population of the world is affected by various comorbidities like diabetes mellitus, hypertension, COPD, obesity, etc which makes a subset of the population more vulnerable. This vulnerable population is at increased risk for a poor outcome if affected by COVID-19. Hence, we as a society should prioritize this population at risk to avoid adding additional burden to the already overburdened health care system in the present COVID-19 scenario.