Analyzing the emotional impact of COVID-19 with Twitter data: Lessons from a B-VAR analysis on Italy.

The novel coronavirus 2019 revolutionized the way of living and the communication of people making social media a popular tool to express concerns and perceptions. Starting from this context we built an original database based on the Twitter users' emotions shown in the early weeks of the pandemic in Italy. Specifically, using a single index we measured the feelings of four groups of stakeholders (journalists, people, doctors, and politicians), in three groups of Italian regions (0,1,2), grouped according to the impact of the COVID-19 crises as defined by the Conte Government Ministerial Decree (8th March 2020). We then applied B-VAR techniques to analyze the sentiment relationships between the groups of stakeholders in every Region Groups. Results show a high influence of doctors at the beginning of the epidemic in the Group that includes most of Italian regions (Group 0), and in Lombardy that has been the region of Italy hit the most by the pandemic (Group 2). Our outcomes suggest that, given the role played by stakeholders and the COVID-19 magnitude, health policy interventions based on communication strategies may be used as best practices to develop regional mitigation plans for the containment and contrast of epidemiological emergencies.

Text Emotion Analysis Using Deep Learning For COVID- 19 Dataset

Managing mental health and psychological well-being is just as critical as managing physical health throughout COVID-19. The difficulty of detecting, classifying, and quantifying emotions in text in any form are addressed in this study. We consider English text collected from social media sites such as Twitter and various Kaggle datasets that can provide information useful in a variety of ways, particularly opinion mining. However, analysing and categorising text based on emotions is a difficult task and might be thought of as a more advanced kind of Sentiment Analysis. This work provides a system for categorising text into three types of emotions: positive, negative, and neutral. This analysis can be utilized by authorities to better understand people's mental health and to make appropriate policy decisions to combat the coronavirus, which is hurting the world's social well-being and economy. © 2022 IEEE.

Tell Me About It: Adolescent Self-Disclosure with an Online Robot for Mental Health

Self-disclosure to a social robot is a mental health intervention that can decrease stress for adolescents. Online digital robots provide the potential to scale this intervention especially in COVID-19 social distancing situations. However, self-disclosure interactions with digital social robots remain relatively unexplored. We conducted two online self-disclosure studies with adolescents (13-19 years old): our Active Listening Study compared experiences sharing positive, negative, and neutral feelings with a social robot, while our Journaling Study explored differences in sharing stressors by speaking with and without a social robot and by writing. We found that positive prompt tone improved mood while neutral prompt decreased stress, and less negative attitudes toward robots correlate with more qualitatively positive experiences with robot interactions.We also found robot disclosure interactions hold promising potential as a preferred method of self-disclosure over solo speaking, moderated by negative attitudes toward robots. This paper outlines limitations and future work from these studies. © 2023 IEEE Computer Society. All rights reserved.

Sentiment Analysis on Online Education During Covid Pandemic

'Sentiment Analysis on Online Education during Covid Pandemic' is the title of this research. This is the most common method for determining and classifying people's view on any services or products. It is the process to determine if a specific piece of information is positive, negative, or neutral. During COVID-19 pandemic lockdowns, educational institutions close, resulting in online schooling. The impact of online education is explored in this research by studying people's reactions to it. Such feedbacks are collected and analyzed by using Lexicon Approach includes (Text Blob, Vader Analysis, and Senti Word Net) to get a better-analyzed result. © 2022 IEEE.

A Systematic Review on Sentiment Analysis for The Depression Detection During Covid-19 Pandemic

Social media platforms such as Facebook, Twitter and Instagram are powerful tools to express sentiments and emotions across the globe. Researchers use sentiment analysis and its evaluation to reveal the positive, negative and neutral opinions associated with an individual or group. In this study, we have analysed the literature available on sentiment analysis based on various parameters such as publication count, year of publication, country and university wise production and keyword progression. Results reveal that sentiment analysis is a pre-owned field and researchers are contributing to this field since 2008. Collaboration among different countries and universities have also seen during this study with maximum contributions received during 2019. Further this study shows that 149 unique keywords are used by different researchers in their literature. 65 universities have contributed to the literature with the highest number of authors from India. © 2022 IEEE.

Sentimental analysis of Twitter data and Comparison of covid 19 Cases trend Using Machine learning algorithms

Coronavirus was first detected in the year 2019 in Wuhan, China. The disease rapidly spread across the country in a short span of time. The Government had imposed strict rules and restrictions for lockdown and social distancing, work from home, and online classes to prevent the further spread of these covid cases During this phase, the morality of the covid cases was significantly controlled. But the larger population was affected by this. So, the mindset of the people has been changed. Sentimental analysis is an opinion mining approach to NLP which is used to detect and categorize the data as positive, negative, and neutral. In a situation like the COVID pandemic, one must stay in a positive mindset. In our project, we are implementing sentimental analysis using the Random Forest algorithm along with comparing the trend in variation of COVID 19 cases using the LSTM and KNN algorithms. © 2022 IEEE.

Comparing Twitter Sentiment and United States COVID-19 Vaccination Rates

As more people use social media as a source of news and information, it is important to understand its impact on individual health decisions. This article compares the sentiment expressed in COVID-19 related tweets with national rates for first dose vaccinations as recorded by the Centers for Disease Control and Prevention. To conduct the study, the text from over 570,000 COVID-related tweets from January 2021 to December 2021 was captured. The tweets were segregated by month and Google Cloud's Natural Language API was used determine the sentiment in each tweet, with each post labeled as having positive, negative, or neutral sentiment. Overall, there was greater prevalence of negative sentiment as compared with positive sentiment during the period of review, with 45% of tweets negative, 33% positive and 22% neutral. The number of positive and negative tweets was more balanced in the early months of 2021 (when the vaccine was first available) and became decidedly more negative in the later part of the year, as misinformation about the vaccines spread prolifically on social media. This comparison of the tweet sentiment to first-time vaccine doses in the US shows that misinformation about vaccines on social media appears to have had an impact on behavior. Vaccine adoption declined significantly in the latter half of 2021, even as vaccines and information from public health officials regarding their efficacy became more available to the general public. These findings are validated by subsequent analysis of word usage by month, with positive comments about vaccines and vaccination in January through May coinciding with high vaccination rates, and a negative conversational shift to variants, increased deaths and suspicion about vaccine safety and effectiveness later in the year during a stagnation period in vaccinations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Comparing Emotion and Sentiment Analysis Tools on Italian anti-vaccination for COVID-19 posts

Since the beginning of the vaccination campaign against Covid-19 in our country, resistance to vaccination has emerged on the part of a not negligible portion of the Italian population. Emotions (such as sadness, fear, etc.) and the polarity (positive/negative) of an opinion published on social media are essential for analyzing people's position towards a topic. For this reason, we applied two Natural Language Processing tools, FEEL-IT and SentIta, to a few thousands of social networks posts against the COVID-19 vaccine or specifically the booster shot. We find out some significant insights about the prevalent emotions among users and propose to combine the outputs of the tools in order to increase the classification performance of an opinion according to three possible sentiments (positive/neutral/negative). © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Agenda Analysis of the Covid-19 News with Latent Dirichlet Allocation

This study interprets the themes obtained as a result of the analysis of the internet news published during the Covid-19 pandemic in our country with Latent Dirichlet Allocation method. Apart from topic modeling, news documents were also subjected to category-based sentiment analysis and time-lapse graphics of published positive, negative and neutral news were shared. For this purpose, 37.724 news texts published in 5 different categories were collected. The period subject to analysis is December 2019 - February 2021. Although the effect of the virus has been alleviated at the moment, the themes that were on the agenda during the period when the effect of the virus was highest could be seen and the results were interpreted. © 2022 IEEE.

A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic

Sentiment analysis is a process of extracting opinions into the positive, negative, or neutral categories from a pool of text using Natural Language Processing (NLP). In the recent era, our society is swiftly moving towards virtual platforms by joining virtual communities. Social media such as Facebook, Twitter, WhatsApp, etc are playing a very vital role in developing virtual communities. A pandemic situation like COVID-19 accelerated people's involvement in social sites to express their concerns or views regarding crucial issues. Mining public sentiment from these social sites especially from Twitter will help various organizations to understand the people's thoughts about the COVID-19 pandemic and to take necessary steps as well. To analyze the public sentiment from COVID-19 tweets is the main objective of our study. We proposed a deep learning architecture based on Bidirectional Gated Recurrent Unit (BiGRU) to accomplish our objective. We developed two different corpora from unlabelled and labeled COVID-19 tweets and use the unlabelled corpus to build an improved labeled corpus. Our proposed architecture draws a better accuracy of 87% on the improved labeled corpus for mining public sentiment from COVID-19 tweets. © 2022 IEEE.

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis.

BACKGROUND: Tuberculosis (TB) had been the leading lethal infectious disease worldwide for a long time (2014-2019) until the COVID-19 global pandemic, and it is still one of the top 10 death causes worldwide. One important reason why there are so many TB patients and death cases in the world is because of the difficulties in precise diagnosis of TB using common detection methods, especially for some smear-negative pulmonary tuberculosis (SNPT) cases. The rapid development of metabolome and machine learning offers a great opportunity for precision diagnosis of TB. However, the metabolite biomarkers for the precision diagnosis of smear-positive and smear-negative pulmonary tuberculosis (SPPT/SNPT) remain to be uncovered. In this study, we combined metabolomics and clinical indicators with machine learning to screen out newly diagnostic biomarkers for the precise identification of SPPT and SNPT patients. METHODS: Untargeted plasma metabolomic profiling was performed for 27 SPPT patients, 37 SNPT patients and controls. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was then conducted to screen differential metabolites among the three groups. Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, "caret" R package, "e1071" R package and "Tensorflow" Python package, respectively. RESULTS: Metabolomic analysis revealed significant enrichment of fatty acid and amino acid metabolites in the plasma of SPPT and SNPT patients, where SPPT samples showed a more serious dysfunction in fatty acid and amino acid metabolisms. Further RF analysis revealed four optimized diagnostic biomarker combinations including ten features (two lipid/lipid-like molecules and seven organic acids/derivatives, and one clinical indicator) for the identification of SPPT, SNPT patients and controls with high accuracy (83-93%), which were further verified by SVM and MLP. Among them, MLP displayed the best classification performance on simultaneously precise identification of the three groups (94.74%), suggesting the advantage of MLP over RF/SVM to some extent. CONCLUSIONS: Our findings reveal plasma metabolomic characteristics of SPPT and SNPT patients, provide some novel promising diagnostic markers for precision diagnosis of various types of TB, and show the potential of machine learning in screening out biomarkers from big data.

Multiple Efficient Data Mining Algorithms with Genetic Selection for Prediction of SARS-CoV2

In this paper, a multiple efficient data mining algorithms with feature selection algorithm for the prediction of SARS- CoV2(covid) is presented. Multiple efficient data mining are composed of a set of algorithms, which are reliable and simple that used to generate many predictions (positive, negative) under various conditions such as random forest, support vector machine, and logistic regression. In data mining, feature selection is a key step in the pre-processing of data. The basic premise of feature selection appears to be to select a subset of potential features by removing characteristics that have little predictive value as well as features that are highly correlated and redundant. Selection of significant features from COVID data is accomplished using genetic feature selection techniques. The final prediction can be improved by combining data mining techniques with a genetic feature selection algorithm in an intelligent method. It looks like the simulations made good guesses about the values in the validation data set in terms of precision, recall, F-rneasure, and accuracy. In fact, the suggested model's prediction errors are much smaller than those of traditional methods. © 2022 IEEE.

Visualization of the Social Atmosphere Using Comments on News Sites

When an emergency such as an infectious disease or natural disaster occurs, a negative atmosphere will usually spread throughout society—increasing people’s dissatisfaction and anxiety. Because of this, it is rather difficult to thoroughly investigate the actual situation. However, people can post sentimental comments on news sites, allowing for their attitudes either for or against the topics to be better observed. This study extracts the positive, negative, and neutral comments by using sentiment analysis. Then, the social atmosphere is visualized by calculating the approval rating of the comments. This methodology is demonstrated in articles regarding COVID-19. The large volume of comments about two topics, Go To campaigns and PCR tests, were analyzed by using ML-Ask to classify the comments into three categories: negative, positive, and neutral. The results indicate that the social atmosphere about the Go To campaigns tended to be negative. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Sentiment Analysis using COVID-19 Twitter Data

The COVID-19 pandemic has essentially transformed the way millions of people across the world live their life. As offices remained closed for months, employees expressed conflicting sentiments on the work from home culture. People worldwide now use social media platforms such as Twitter to talk about their daily lives. This study aims to gage the public’s sentiment on working from home/remote locations during the COVID-19 pandemic by tracking their opinions on Twitter. It is essential to study these trends at this point in the pandemic as organizations should decide whether to continue remote work indefinitely or reopen offices and workspaces, depending on productivity, and employee satisfaction. Tweets posted in the live Twitter timeline is used to generate the set of data and accessed through Tweepy API. About 2 lakh tweets relevant to the remote work during the pandemic were tokenized and then passed to Naive Bayes classifier that classifies the sentiments positive, negative, neutral to every tweet. Our findings emphasize on population sentiment which is the effects of the COVID-19 pandemic, especially resulting from the work from home policy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Deep Learning Approach to Predict COVID-19 Through Cough Analysis Using CNN-BiDirectional LSTM

COVID-19 is deadly contagious and with new variants forming every day, people are at great risk. 29.9 million cases have been reported in India so far and the range goes up to 179 million cases worldwide. Due to the limited number of test centers and the ever-increasing cases, the equipment and the lab technicians are getting outnumbered. They are also at constant risk of getting infected. The scientists have been working on analyzing coughs using machine learning and this technology has been successful in analyzing different types of coughs and classifying them into respective categories. Using this, a deep learning model is created that analyses the recorded cough sample and classifies them into their respective category. A CNN-Bidirectional LSTM is used to create this model and run it on the Covid-sounds dataset provided by Cambridge University. The Covid-sounds dataset has both breathing and cough samples of positive, negative, and other respiratory diseases which might alter or cause cough. This data is pre-processed and used as cough samples for the model. This model has outperformed other models which used the same dataset. © 2022 IEEE.

Impact of Covid-19 Pandemic on Recruitment Process and its Sentiment Analysis

Covid-19 Pandemic has affected the entire human kind in a devastating way and the effects it has caused to different sectors of life are not yet gauged. In this research paper we are investigating the effects the pandemic has had on the recruitment process in the campuses and on students and employers. The various perspectives of the recruitment process and the changes the students and the trainers had to make in the process are considered here. The various stakeholders of the process like the human resource (HR) managers, placement officers, students undergoing placements and students who are recently placed are considered here to get a 360-degree perspective of the same. A sentiment analysis of the data procured is performed to categorize the opinions into “Positive”, “Negative” and “Neutral” from the different stakeholders. We are using the rule based approach here to find the polarity of the sentences based on their scores. The study has disclosed the apprehensions of all the stakeholders regarding the shift from offline to online mode of recruitment and also training. © 2021 IEEE