Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.

The aim of this article is to examine the impacts of Coronavirus Disease -19 (Covid-19) vaccines on economic condition and sustainable development goals. In other words, we are going to study the economic condition during Covid19. We have studied the economic costs of pandemic, benefits in terms of gross domestic product (GDP), public finances and employment, investment on vaccines around the world, progress and totally the economic impacts of vaccines and the impacts of emerging markets (EM) on achieving sustainable development goals (SDGs), including no poverty, good health and well-being, zero hunger, reduced inequality etc. The importance of emerging economies in reducing the harmful effects of the Corona has also been noted. We have tried to do experimental results and forecast daily new death cases from Feb-2020 to Aug-2021 in Iran using Artificial Neural Network (ANN) and Beetle Antennae Search (BAS) algorithm as a case study with econometric models and regression analysis. The findings show that Covid19 has had devastating economic and health effects on the world, and the vaccine can be very helpful in eliminating these effects specially in long-term. We observed that there is inequality in the distribution of Corona vaccines in rich countries compared to poor which EM can decrease the gap between them. The results show that both models (i.e., Artificial intelligence (AI) and econometric models) almost have the same results but AI optimization models can robust the model and prediction. The main contribution of this article is that we have surveyed the impacts of vaccination from socio-economic viewpoint not just report some facts and truth. We have surveyed the impacts of vaccines on sustainable development goals and the role of EM in achieving SDGs. In addition to using the theoretical framework, we have also used quantitative and empirical results that have rarely been seen in other articles.

In this research work, we have applied “Lipinski’s RO5” for pharmacokinetics (PK) study and to predict the activity of peptidic HIV-1 protease inhibitors. Peptidic HIV-1-PRIs have been taken from literature with their observed biological activities (OBAs) in term of IC50. The logarithms of the inverse of IC50 have been used as biological end point o(log1/C) in the study. For calculation of physicochemical parameters, the molecular modeling and geometry optimization of all the derivatives have been carried out with CAChe Pro software using semiempirical PM3 method. Prediction of the biological activity of the inhibitors has shown that the best QSAR model is constructed from pharmacokinetic properties, molecular weight and hydrogen bond acceptor. This also proved that these properties play important role to describe the PKs of the drugs. On the basis of the derived models one can build up a theoretical basis to access the biological activity of the compounds of the same series.

Background: Coronavirus disease has caused global turmoil especially causing huge impact on human life all over the world. Current reports states more than 3 million people have lost life and more than 160 million people are known to be suspected with the SARS-CoV-2. Transmission and disease incidence rates are indicators to assess the seriousness of COVID-19 pandemic and studies to understand the factors that aid in this direction are very vital to curb the disease. Methods: The study intends to discover the relationship by performing statistical analysis using correlation and multiple linear regression analysis between the variable’s population density, temperature, relative humidity, and active time of virus and find out the parameters that predict the cases reported per million population in 83 countries. Results: Analysis indicates active time of virus in days is very positively associated with the COVID -19 cases in all the countries r = .604, p < .01. Active time of virus shows strong negative correlation with temperature r = -.930, p < .01 revealing that rise in temperature will reduce the virus activity in the population. Together, these variables will account for 36.2% variance in the cases per million population with no significant prediction estimated from any factor. Conclusion: The study outcomes clearly state that population density alone is insufficient to estimate the extent of influence on COVID -19 cases as the number of persons living per sq. km of land is a dynamic quantity tend to fluctuate over time and space due to migration of population. In conjunction to the previous studies reported on the environmental and climatic factors influencing the cases reported, population dynamics does not show much significance on the disease spread and incidence. Contribution: The rise in confirmed cases and the high incidence rate reported in countries can be attributed to the active time of virus life expectancy as there is a positive correlation observed between the COVID-19 cases reported and the virus active time in the examined countries. Also, environment and climatic factors play a role in modulating the infection and transmission rate with less significant influence of population density on the COVID-19.