Sentiment Analysis of COVID-19 Tweets Using TextBlob and Machine Learning Classifiers: An Evaluation to Show How COVID-19 Opinions Is Influencing Psychological Reactions of People's Behavior in Social Media

Social media are the influential Internet community for general netizens to disseminate information, views, and opinions extensively. Recently, the research on Sentiment analysis has intensified due to the vast amount of data obtained from these numerous social networking platforms. During this COVID-19 pandemic, one of the great social networking sites viz. Twitter has experienced a significant increase in online posts and reviews concerning COVID-19 related tweets. In the view of Twitter, the spread of news concerning coronavirus variants has impacted many aspects of the public by providing messages around different issues and opinions to its potential users. In this study, we have used Twitter as a source of data for searching the index keywords and hashtag versions for the terms like "Pandemic”, "Coronavirus”, "COVID-19”, "SARS-CoV-2”, and "Omicron”, and we have examined the psychological and emotional impact it had on the public. This paper provides analytical information about people's perceptions of coronavirus (tweets were collected from March 2020 to December 2021). With those tweets, sentiment analysis was performed using the TextBlob text processing python module to acquire the people's subjective data (opinions and feelings) polarity concerning the effects of coronavirus. Furthermore, for effective text classification, we have applied classification methodologies like Support Vector Machine (SVM), K Nearest Neighbor (KNN), Logistic Regression (LR), and Decision Tree (DT). The SVM concerning feature extraction produced the results with 99.49% accuracy. This study assists the government organizations in attaining COVID-19 insights by implying public mental health sentiments in social networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analysis of Student Satisfaction on Virtual Learning Platforms During COVID-19

As a result of the global spread of COVID-19, e-Learning has recently experienced extraordinary growth. Many educational sectors have made the transition from traditional classroom learning to virtual learning via various online platforms. In this epidemic, virtual learning has enabled all schools and universities to continue to provide education. This rapidly growing alternative modality necessitates the provision of robust and high-quality education. It is also important to figure out whether online learning satisfies the needs of pupils. Even if learning has become easier, many people still confront difficulties, poor connectivity and e-platform. This study aims to identify the students' satisfaction by conducting a survey and analyzing it by means of data analysis and data visualization.

Analysis of the Covid-19 Process in Terms of Health Managers

In this study, sentiment analysis was conducted on the data of the Covid-19 epidemic process from the official twitter account of the Republic of Turkey Fahrettin Koca, Minister of Health, @drfahrettinkoca (SO) and the Twitter account of the @WHO (World Health Organization). First of all, twitter data was obtained and necessary arrangements were made for analysis. Then, tweets were shown with a word cloud and it was determined which words were used more frequently. Afterwards, sentiment analysis was performed on the data using the TextBlob library. In addition, it has been found out which subjects are focused on tweets sent from SO and @WHO (World Health Organization) accounts with the LDA algorithm. It has been seen that positive tweets were sent from both accounts, giving positive messages to the society. © 2022 IEEE.

Sentiment Classification Against the Public Activity Restrictions Policy in Jakarta Using Machine Learning Models

To stop the spread of the COVID-19, the Indonesian government implemented community activities restrictions enforcement (in Indonesian language: Pemberlakuan Pembatasan Kegiatan Masyarakat or PPKM) starting from January 2021. The term PPKM applied are PPKM Mikro (in Indonesian language) or Micro PPKM, PPKM Darurat (in Indonesian language) or Emergency PPKM, and PPKM Level 1-4 or Level 1-4 PPKM. On the other hand, the existing research mostly used Twitter as the data source to do sentiment classification. Therefore, we aimed to classify social media comments on Facebook and YouTube on Level 1-4 PPKM policy in Jakarta. We used "PPKM Jakarta"as the keyword topic in August - September 2021 when Level 1-4 PPKM was ongoing. In addition, we compared datasets composition, machine learning models, and features extraction. Random Forest, Naive Bayes, and Logistic Regression were performed as the machine learning models due to they were the top three models on the previous research. We extracted word unigram, word bigram, character trigram, and character quadrigram as the feature extraction. The highest average F-measure was obtained with a 79.6% score of the Logistic Regression model using character quadrigram extraction. We found that comments from Facebook and YouTube were dominated by neutral sentiment (49.8%) with this setup. It means the people of Jakarta started to trust the government in handling the COVID-19 pandemic. Through word cloud analysis, it is recommended that social assistance be reviewed for those directly affected. © 2021 IEEE.

Quality Design for the COVID-19 Pandemic: Use of a Web Scraping Technique on Text Comments and Quality Ratings from Multiple Online Sources

This study explores the main determinants of airline satisfaction by integrating data from two online survey sources collected via the use of a web scraping technique on text comments and quality ratings to determine service recovery procedures for the aviation industry during the COVID-19 pandemic. The text analysis technique provides information on how passengers rate service attributes (high or low) by generating clusters of the most frequent comments (WordCloud). The results suggest that satisfied passengers highlight empathy and responsive service, while negative reviews suggest frequent instances of poor operational performance, such as refund processes, rescheduling, and system breakdowns. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Artificial Intelligence Based Analysis of Positive and Negative Tweets Towards COVID-19 Vaccines

Today, the whole world is facing a biggest challenge in the form of coronavirus. The spread of COVID has caused health concerns worldwide. Considering this, there is an increase in the global efforts for the development of the COVID. The widespread provision of the vaccine is the major requirement in achieving the immunity against coronavirus. For this purpose, the public sentiments towards the vaccine campaign must be analysed. With the help of social media services, people are freely sharing their feelings and sentiments through posts, reviews or tweets. In this research, we have used advanced artificial intelligence methods for analysing the public sentiments towards vaccine campaigns. For this purpose, we used twitter data freely available on the Kaggle website and performed basic preprocessing steps. We used natural language processing (NLP) techniques such as TextBlob() and word cloud in order to find the polarity of the tweets to categorize them in seven different classes and find the most frequent keywords respectively. We used BERT model for sentimental analysis to understand the people's mental state by studying their opinion and behaviour towards vaccines. Hence, the artificial intelligence based social network analysis must be considered for performing and analyzing the public sentiments towards any trending topic, pandemic or any other worldwide or local issue. Such methods can help to develop the trust of people towards vaccine campaigns timely and help to provide the proper administration of vaccines at large scale. © 2021 IEEE.