Pandemic Outbreak Time: Evaluation of Public Tweet Opinion by Machine Learning

In this work, a Twitter data-set was utilized to do sentiment analysis of people's thoughts on the corona-virus (COVID-19) period, which is a major concern throughout the world these days, impacting a number of nations. To better understand people's feelings about the epidemic, machine learning approaches (mla) and sentiment methodology such as Bert Model (BMO), Naive_Bayes_Bernoulli (nBB), Multi Nominal Naive_Bayes (mnNB), Support_ Vector_Machine (svM), Logistic_Regression (IR), Gradient_Boosting_ Classifier (gbR), Decision Tree Classifiers (dtC), K N eighbors(knN) and Random Forest Classifier (rfC) have been presented in this work. Also, we have classified that which Classifiers provides highest accuracy. Additionally, in this paper, we also analysis from the data set, the most that has been tweeted (hashtag), positive, negative as well as neutral with data visualization in the Covid-19 epidemic time. © 2022 IEEE.

Twitter Opinion Mining on COVID-19 Vaccinations by Machine Learning Presence

The COVID-19 pandemic has effectively shut down the whole planet. Most countries have now suspended lockdowns or semi-lockdowns, although lockdowns still exist in many countries. The coronavirus epidemic has disrupted people's daily lives. People from all across the globe have flocked to social media to voice their thoughts and feelings on the phenomenon that has gone viral. In a very short period of time, the social networking site Twitter saw an extraordinary rise in tweets pertaining to the novel coronavirus. With the discovery of several vaccines for the virus, the new year of 2021 brought with it new hope. A global vaccine campaign is under way, and we anticipate that the world will quickly recover from this pandemic and return to normalcy. This paper is devoted to the vaccination drive's tweets. This is used to predict the attitude of tweets on vaccinations. We have taken note of how sentiment changes over time, with respect to vaccination, through the general people who tweeted. For analysis, VADER and LSTM, Z-score, have been used. Additionally, with vaccine data visualization, the most common positive and negative, all hashtags, and the source of the data have been analyzed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.