Comparative Analysis of Classification Techniques on Olympic Games Datasets

Olympic game is a prestigious ceremony that occurs after every four years. However, due to the spread of coronavirus in 2020, the game was held in 2021, which is post-Covid. The main aim of this research is to find out if there was a difference in the performance of nations in Rio 2016 Olympics (pre-Covid) and Tokyo 2020 Olympics (post-Covid). Statistical analysis is carried out to find the correlation between the different variables. One of the highly correlated variables (Gold Tally) is removed while performing the classification analysis. The idea is to see if the classifiers are able to do the comparative analysis without it or not. The classification algorithms utilized in this research are Decision Table, Decision Tree, Naïve Bayes, and Random Forest. The datasets used in this research are imbalanced sets, which were later transformed to balance sets through under-sampling. Random Forest was able to give 100% accuracy in both datasets whereas the True Positive Rate (TPR) was also 100%. After doing the comparative analysis it was found that irrespective of pre and post-Covid, the performance of athletes did not change. This paves the way for other researchers to investigate if Covid had any impact on the performance of the athletes or not. In the future, more vast variables will be investigated to do a more detailed comparative analysis. © 2022 IEEE.

Tweet Classification and Sentiment Analysis of Covid 19 Epidemic by Applying Hybrid Based Techniques

World wide spread of COVID-19 pandemic, is throttling the normal life nearly for two years and claiming millions of life all over the globe. Starting from Wuhan of China it crosses more than 200 countries, thereby imposing a overwhelming challenge to health care system. On the other hand, there has been unprecedented advancement of the social media, namely, Twitter, Facebook, WhatsApp and Instagram etc. in an exponential manner. The essence of this paper is to extract and elucidate the opinion or sentiments of the people all around the globe regarding Coronavirus pandemic based on Twitter data. The analysis are based on both lexicon-based approach followed by machine learning algorithms and aims to express the state-of-the-art of the sentiment analysis on the current Coronavirus epidemic prevailing in the entire world and the awareness of the people regarding the disease, its symptoms and impact followed by the preventive measures that need to be undertaken. © 2022 IEEE.

Impacts of COVID-19 Pandemic Crisis in the Transportation Sector: A Classification Analysis in Regard with Preferred Modes of Transportation Using Random Forest Algorithm

The study observes the Pandemic Crisis (Covid 19) that resulted in impacts on the Transportation category in the area National Capital Region. Public transportation is an important aspect of human's ability to travel to different places whether its personal or business purpose, it's a part of life that people take for granted and can't be taken away easily. But due to the pandemic era, people have been careful in their choices, which resulted in the change standard when it comes to public transportation choices. With that said, to understand and observe these impacts, a scenario must be made such as before and after the pandemic designed as an environment for the study to take root. The study has used machine learning called Random Forest Algorithm with the used several parameters to create a prediction model. As for the method in gathering data, a survey of Google Form is utilized to gather 200 participants of the National Capital Region with varying parameters for their choice of public transportation. The machine algorithm has shown satisfactory accuracy of 89.88% and 88.88%. As an important note, it is observed that travel expense has more impact on public transportation choices than other parameters. The Random Forest Algorithm has been utilized in creating classification types of models and can help future researchers improve the machine learning approach. © 2021 IEEE.

Analysing Tweets on COVID-19 Vaccine: A Text Mining Approach

The COVID-19 pandemic has caused large scale health, economic, and social crisis. Scientists throughout the globe have been working on producing effective vaccines to combat this pandemic. COVID-19 vaccine release started in 2020, and low take-up rates among the public have been observed initially. There has been a soar in social media data on vaccines. This paper presents a comprehensive analysis of COVID-19 vaccine-related tweets. Sentiments shared by people through tweets and common topics have been extracted using classification and sentiment analysis. Our results showed a higher negative sentiment when the pandemic was declared, and it gradually changed to positive with the COVID-19 vaccine development/rollout. Tweet sentiment analysis offers health departments around the globe a quick sense of public sentiment towards the vaccine. Dominant topics or areas of concern have been identified using topic modelling that might need to be addressed. © 2022 IEEE.