Apical-Out Human Airway Organoids Modeling SARS-CoV-2 Infection.

The respiratory epithelium, particularly the airway epithelium, is the primary infection site for respiratory pathogens. The apical surface of epithelial cells is constantly exposed to external stimuli including invading pathogens. Efforts have been made to establish organoid cultures to recapitulate the human respiratory tract. However, a robust and simple model with an easily accessible apical surface would benefit respiratory research. Here, we report the generation and characterization of apical-out airway organoids from the long-term expandable lung organoids that we previously established. The apical-out airway organoids morphologically and functionally recapitulated the human airway epithelium at a comparable level to the apical-in airway organoids. Moreover, apical-out airway organoids sustained productive and multicycle replication of SARS-CoV-2, and accurately recapitulated the higher infectivity and replicative fitness of the Omicron variants BA.5 and B.1.1.529 and an ancestral virus. In conclusion, we established a physiologically relevant and convenient apical-out airway organoid model for studying respiratory biology and diseases.

When Unipolarity Dies: The Russo- Ukrainian War, the End of the Neoliberal Order and Asian Security in the 2020s

The end of the Cold War and the collapse of the Soviet Union allowed for a period of US-centered unipolarity in global affairs. This period has ended;it will not return. Moreover, the delicate neoliberal world order crafted by the United States and its allies is collapsing, unable to endure the stress of the COVID-19 crisis and its aftermath. The rise of China does not, however, mean that the world is returning to a long period of bipolarity --reminiscent of the US-Soviet Cold War. Rather, the United States and China simply happen to be far greater than any of their potential competitors at present-the globe is in a condition of "incomplete multi-polarity." The multipolar system is maturing rapidly, however, and it is to be expected that an increasing number of great and medium powers will pursue their interests unilaterally and assertively. This period of deepening multi-polarity is dangerous. It may plausibly culminate in a Third World War. This analysis examines immediate and longer-term dangers accompanying the new multi-polarity, with particular emphasis on how the security of East and South Asia is inextricably linked.

Negative Sentiment Modeling and Public Legal Liability from Urban Green Space: A Framework for Policy Action in China

This paper intends to optimize the urban green space (UGS) management and implementation strategies by analyzing climate change models and reviewing economic, energy, and public health policies. This paper studies the public perception of climate change-induced public health emergency (PHE) in China by surveying online public comments. Specifically, it looks into public health perception, anxiety perception, relative deprivation, and emotional polarity from public online comments. The following conclusions are drawn through the empirical test of 179 questionnaires. The findings revealed that health risk perception has a positive predictive effect on relative deprivation and anxiety perception. The higher the health risk perception, the stronger the relative deprivation and anxiety are. Anxiety perception and relative deprivation have mediating effects in the model. In addition, the main research method adopts a questionnaire survey. The mediating effect between each variable is further studied. This paper analyzes the citizens' right to health and public health protection under climate change, and explains public risk perception and anxiety perception. Meanwhile, the evaluation cases are used to analyze the public health and UGS construction strategies to suggest climate compensation laws and improve the urban greening rate. This finding has practical reference value for promoting the deep integration of UGS and public health. It can promote the development and planning of UGS under climate change and biodiversity loss and has significant reference value for improving negative emotions and the public legal liability system.

Computational predictors of the predominant protein function: SARS-CoV-2 case

In this chapter, we describe the main molecular features of SARS-CoV-2 that cause COVID-19 disease, as well as a high-efficiency computational prediction called Polarity Index Method®. We also introduce a molecular classification of the RNA virus and DNA virus families and two main classifications: supervised and non-supervised algorithms of the predictions of the predominant function of proteins. Finally, some results obtained by the proposed non-supervised method are given, as well as some particularities found about the linear representation of proteins. © 2022 Scrivener Publishing LLC.

Crowd Control, Planning, and Prediction Using Sentiment Analysis: An Alert System for City Authorities

Modern means of communication, economic crises, and political decisions play imperative roles in reshaping political and administrative systems throughout the world. Twitter, a micro-blogging website, has gained paramount importance in terms of public opinion-sharing. Manual intelligence of law enforcement agencies (i.e., in changing situations) cannot cope in real time. Thus, to address this problem, we built an alert system for government authorities in the province of Punjab, Pakistan. The alert system gathers real-time data from Twitter in English and Roman Urdu about forthcoming gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.). To determine public sentiment regarding upcoming anti-government gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.), the alert system determines the polarity of tweets. Using keywords, the system provides information for future gatherings by extracting the entities like date, time, and location from Twitter data obtained in real time. Our system was trained and tested with different machine learning (ML) algorithms, such as random forest (RF), decision tree (DT), support vector machine (SVM), multinomial naïve Bayes (MNB), and Gaussian naïve Bayes (GNB), along with two vectorization techniques, i.e., term frequency–inverse document frequency (TFIDF) and count vectorization. Moreover, this paper compares the accuracy results of sentiment analysis (SA) of Twitter data by applying supervised machine learning (ML) algorithms. In our research experiment, we used two data sets, i.e., a small data set of 1000 tweets and a large data set of 4000 tweets. Results showed that RF along with count vectorization performed best for the small data set with an accuracy of 82%;with the large data set, MNB along with count vectorization outperformed all other classifiers with an accuracy of 75%. Additionally, language models, e.g., bigram and trigram, were used to generate the word clouds of positive and negative words to visualize the most frequently used words.

COVID-19 Public Opinion: A Twitter Healthcare Data Processing Using Machine Learning Methodologies.

The COVID-19 pandemic has shattered the whole world, and due to this, millions of people have posted their sentiments toward the pandemic on different social media platforms. This resulted in a huge information flow on social media and attracted many research studies aimed at extracting useful information to understand the sentiments. This paper analyses data imported from the Twitter API for the healthcare sector, emphasizing sub-domains, such as vaccines, post-COVID-19 health issues and healthcare service providers. The main objective of this research is to analyze machine learning models for classifying the sentiments of people and analyzing the direction of polarity by considering the views of the majority of people. The inferences drawn from this analysis may be useful for concerned authorities as they work to make appropriate policy decisions and strategic decisions. Various machine learning models were developed to extract the actual emotions, and results show that the support vector machine model outperforms with an average accuracy of 82.67% compared with the logistic regression, random forest, multinomial naïve Bayes and long short-term memory models, which present 78%, 77%, 68.67% and 75% accuracy, respectively.

Indonesian perceptions on online learning amidst COVID-19: a Twitter sentiment analysis

The objective of this research is to uncover Indonesians' perceptions of online learning during the COVID-19 pandemic by determining the polarity of language texts (positive, neutral, or negative) compiled from Twitter. The data required to reveal the Indonesian people's opinion on online learning during the COVID-19 pandemic is a tweet on Twitter with the hashtag #Pembelajaran daring (Online learning);#Pembelajaran jarak jauh (distance learning);#Belajar dari rumah (learning from home);#Belajar di rumah (learning in the home) (learning at home). The time frame for collecting these tweets is March 2020 to November 2021. The data was then analyzed using lexicon analysis and analytical tools that used Part of Speech Tagging. According to the results, 77.58% of the tweets are positive, 17.97% are negative, and the remainder are neutral. People prefer to refer to learning support, teachers, schools, education, students, and distance learning. Distance learning is the most positively received category among online learning. However, learning support is the most widely discussed topic among the general public. The overwhelming positive sentiment across all categories suggests that the majority of Indonesians have high hopes for online learning during the pandemic. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Overview of Rest-Mex at IberLEF 2021: Recommendation System for Text Mexican Tourism

This paper presents the framework and results from the Rest-Mex track at IberLEF 2021. This track considered two tasks: Recommendation System and Sentiment Analysis, using texts from Mexican touristic places. The Recommendation System task consists in predicting the degree of satisfaction that a tourist may have when recommending a destination of Nayarit, Mexico, based on places visited by the tourists and their opinions. On the other hand, the Sentiment Analysis task predicts the polarity of an opinion issued by a tourist who traveled to the most representative places in Guanajuato, Mexico. For both tasks, we have built new corpora considering Spanish opinions from the TripAdvisor website. This paper compares and discusses the results of the participants for both tasks.

Hemispheric-wide climate response to regional COVID-19-related aerosol emission reductions: the prominent role of atmospheric circulation adjustments

The national and global restrictions in response to the COVID-19 pandemic led to a sudden, albeit temporary, emission reduction of many greenhouse gases (GHGs) and anthropogenic aerosols, whose near-term climate impact were previously found to be negligible when focusing on global- and/or annual-mean scales. Our study aims to investigate the monthly scale coupled climate-and-circulation response to regional, COVID-19-related aerosol emission reductions, using the output from 10 Earth system models participating in the Covid model intercomparison project (CovidMIP). We focus on January–February and March–May 2020, which represent the seasons of largest emission changes in sulfate (SO2) and black carbon (BC). During January–February (JF), a marked decrease in aerosol emissions over eastern China, the main emission region, resulted in a lower aerosol burden, leading to an increase in surface downwelling radiation and ensuing surface warming. Regional sea-level pressure and circulation adjustments drive a precipitation increase over the Maritime Continent, embedded in a negative Pacific Decadal Oscillation (PDO)- and/or El Niño–Southern Oscillation (ENSO)-like response over the Pacific, in turn associated with a northwestward displacement and zonal shrinking of the Indo-Pacific Walker cell. Remote climate anomalies across the Northern Hemisphere, including a weakening of the Siberian High and Aleutian Low, as well as anomalous temperature patterns in the northern mid-latitudes, arise primarily as a result of stationary Rossby wave trains generated over East Asia. The anomalous climate pattern and driving dynamical mechanism reverse polarity between JF and MAM (March–May) 2020, which is shown to be consistent with an underlying shift of the dominant region of SO2 emission reduction from eastern China in JF to India in MAM. Our findings highlight the prominent role of large-scale dynamical adjustments in generating a hemispheric-wide aerosol climate imprint even on short timescales, which are largely consistent with longer-term (decadal) trends. Furthermore, our analysis shows the sensitivity of the climate response to the geographical location of the aerosol emission region, even after relatively small, but abrupt, emission changes. Scientific advances in understanding the climate impact of regional aerosol perturbations, especially the rapidly evolving emissions over China and India, are critically needed to reduce current uncertainties in near-future climate projections and to develop scientifically informed hazard mitigation and adaptation policies.

Reevaluating Synthesizing Sentiment Analysis on COVID-19 Fake News Detection using Spark Dataframe

Some research uses the random forest model and sentiment analysis to detect COVID-19 fake news. However, there is still a research opportunity to apply the method to Indonesian Tweets and reevaluate the feature's performance. Our research aims to reevaluate synthesizing the sentiment analysis feature on detecting COVID-19 fake news on Indonesian Tweets by using the Spark Dataframe. We divide the stages of machine learning development into several steps, including collecting data using Tweepy and then applying sentiment polarity scores using Apache Spark. We apply random forest to classify fake news using the Spark MLlib. Further, we use model evaluation calculation through the level of Accuracy, Recall, Precision, and F1. The results show that applying the sentiment polarity calculation to our Tweet dataset labels 148 Tweets with positive sentiments, 118 Tweets with negative sentiments, and 99 Tweets with neutral sentiments. The Pearson correlation coefficient (PCC) feature score of Sentiment equals 0.056 and ranks fifth in the top feature correlation scores list. According to the experimental findings, the random forest model produces Accuracy = 0.787 for both models with sentiment analysis and without sentiment analysis. Which indicates that sentiment analysis provides no significance in the prediction model. © 2022 IEEE.

Transfer Learning using BERT & Comparative Analysis of ML Algorithms for Opinion Mining

Sentiment Analysis is an ongoing field of research in text mining that is concerned with the computational treatment of textual views, sentiments, and subjectivity. It's the task of distinguishing between positive and negative viewpoints, emotions, and assessments. Sentiment analysis has been the topic of intensive research since its inception. During COVID-19, there has been a subtle increase in the usage and the time spent on the social networking sites by people as most of the daily operations have moved online. Moreover, in addition to the illness itself, the pandemic has led to dread, anxiety, stress, concern, repugnance, and poignancy in individuals all around the world. Considering these indicators, experts are paying close attention to Twitter data analysis during this pandemic. BERT is used as a transfer learning model and this work analyses the efficacy of fine-tuning it for the task of opinion mining by comparing it to a baseline model that includes a TF-IDF vectorizer and a Naïve Bayes classifier. Its performance is also compared to that of Naïve Bayes, Logistic Regression, K Nearest Neighbor, Decision Tree and XGBoost classifier. To determine the most effective settings for the BERT model, hyper-parameter tweaking is used. After two epochs of training at a learning rate of le-5 and batch size of 16, the maximum accuracy of 87.6% is attained. These results outperform all of the machine learning models examined in this study. This work tackles a comprehensive overview of the last update in this field. It can be beneficial to scholars in this domain because it encapsulates the most well-known Sentiment analysis methodologies and their comparison in single research work. © 2022 IEEE.

Dictionary Based Gender Identification and Gender Based Sentiment Analysis with Polarized Word2Vec

Each gender is having its special behaviour which can be reflected in every field of social media. During the pandemic of COVID-19, people used twitter to discuss the issues caused by COVID-19 disease. As Twitter does not disclose the gender of the user, in this study we have discussed different kinds of approaches used to identify the gender. From the literature review, it is found that the dictionary-based approaches are the best suitable approach when we are working with the sentiment analysis of unlabelled data. This study is about the analysis of ten kinds of emotions of males and females by which we can observe how they reacted in this pandemic. The research proposes a dictionary-based approach to identify the gender and then analyzed sentiments using the cluster-based approach is applied onto word vectors after multiplying them with sentence’s polarity. The proposed approach is compared with the existing approaches with different data set and found that our proposed approach depicts good accuracy of sentiment analysis of unlabelled gendered data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Sentiment Analysis as Assessment of the COVID-19 Social Assistance Pollemic using Random Forest Algorithm

The government's endeavors in organizing the COVID-19 Social Assistance program often encounter problems and lead to the opinion of many parties. One of the opinions expressed on social media is twitter. Sentiments from these opinions were then analyzed to find out the assessment and discussion of each sentiment that can be used as evaluation material for the Social Assistance program. In this study, the sentiment of each preprocessed text was obtained using a labeling process with an assessment of polarity and subjectivity from TextBlob library. The results of neutral, positive, and negative sentiment assessments were weighted using TFIDF. Words that have been formatted into numeric then classified using the Random Forest algorithm. The parameters in this case were in accordance with the documentation on sklearn. An evaluation of the algorithm was also carried out using the 10 kfold cross validation method as a performance validation of the results of testing each piece of data. The performance obtained is quite satisfactory. © 2022 IEEE.

Textual Dissection of Live Twitter Reviews on Corona Vaccines using Various Machine Learning Algorithms

The most popular hash tag on Twitter in 2020 was #COVID19 Vaccination, which got roughly 400 million notices. In this paper, we examine a worldview for unearth the feeling about COVID-19 inoculations among the public from Twitter. After obtaining the misconceptions and ideas in circulation, we suggest a solution for the same through Machine Learning algorithms. Twitter is a well known microblogging social media website where users distribute their perspectives on any topic(s). The ideology of textual dissection describes how people think about a text. It's the process of categorising tweets into positive and negative groups. Tweepy and TextBlob are Python libraries that can be used to extract and classify Tweets using Machine Learning methods including Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree. The goal is to make analysis, summarization, and classification as straightforward as possible. These computations comprise a positive, negative, or neutral assessment of Twitter data. In light of public perception, we hypothesize the best immunization feasible with maximum antibodies based on public perception through opinion research. © 2022 IEEE.

Aspect-Based Sentiment Analysis of online Reviews for Business Intelligence

The COVID-19 pandemic has affected the patients to a large extent who are in immediate need of medical care. People rely on online reviews shared on review websites to gather information about hospitals like the availability of beds, availability of ventilators, etc. However, these reviews are large in number and unstructured, which makes it difficult to interpret them. Hence, the authors have proposed a methodology that will apply an aspect-based sentiment analysis on the reviews to gain meaningful insights of the hospital based on different aspects like doctor, staff, facilities, etc. For the purpose of empirical validation, a total of 26,071 reviews pertaining to 325 hospitals were scrapped. The authors concluded that the study can be useful for patients as it helps them to select the hospital that best suits them. The study also helps hospital administration to improve the current services according to the needs of the patients. Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Sentiment Dynamics Detection of Online Learning Impact using Hybrid Approach

With the growth of technology, the concept of online learning has grown in popularity. The worldwide epidemic situation (Covid 19) has increased the use of online learning not just in Higher Education Institutions (HEIs), but also at all levels of education (ISED levels). In these hard times, technical advancements have played a greater role in creating awareness of the existence of online learning which has evolved as an alternate avenue for gaining and disseminating knowledge in a systematic way. In this paper we investigate to detect the sentiment dynamics (SD) of tweets related to online education available on twitter platform and deduce conclusions about its impact on student’s emotions. Over one lakh subjective tweets about the world's emerging online education system have been gathered via Twitter. Sentiment analysis was performed on the gathered dataset using the combination of dictionary-based and statistical-based approaches. Based on the findings of this analysis, we can infer the impact of online education and how people's attitudes have changed as a result of changes in the educational system. As a result, we would like to present a better comprehension of the sentiment dynamics of online education adoption. © 2022. Specialusis Ugdymas. All Rights Reserved.

COVID-19 Vaccine Brand Sentiment on Twitter

Online social networks (OSNs) are today a primary way to spread and consume information. Maybe the most important aspect of OSNs, both an opportunity and a weakness, is that OSNs are open: users can post anything, which leads to proliferation of information with various degrees of truthfulness. This impacts the volume of information, trending topics, and sentiment of users vis-à-vis of these topics. Our goal in this work is to analyze the spreading of information in Twitter, volume-wise and sentiment-wise (positive or negative), for COVID-19 vaccines overall, and for some specific brands. Our analysis was carried on over five 10-day time-windows in 2021, starting from February and until October. We also looked at what were the most popular tweets we collected during our predefined time-windows, and, by looking at the retweets counts, we observed how they trended over time. © 2022 ACM.

A Social Media Infodemic-Based Prediction Model for the Number of Severe and Critical COVID-19 Patients in the Lockdown Area.

Accurately predicting the number of severe and critical COVID-19 patients is critical for the treatment and control of the epidemic. Social media data have gained great popularity and widespread application in various research domains. The viral-related infodemic outbreaks have occurred alongside the COVID-19 outbreak. This paper aims to discover trustworthy sources of social media data to improve the prediction performance of severe and critical COVID-19 patients. The innovation of this paper lies in three aspects. First, it builds an improved prediction model based on machine learning. This model helps predict the number of severe and critical COVID-19 patients on a specific urban or regional scale. The effectiveness of the prediction model, shown as accuracy and satisfactory robustness, is verified by a case study of the lockdown in Hubei Province. Second, it finds the transition path of the impact of social media data for predicting the number of severe and critical COVID-19 patients. Third, this paper provides a promising and powerful model for COVID-19 prevention and control. The prediction model can help medical organizations to realize a prediction of COVID-19 severe and critical patients in multi-stage with lead time in specific areas. This model can guide the Centers for Disease Control and Prevention and other clinic institutions to expand the monitoring channels and research methods concerning COVID-19 by using web-based social media data. The model can also facilitate optimal scheduling of medical resources as well as prevention and control policy formulation.

Mappare il racconto, raccontare l'emergenza. Voci digitali dai territori 1

Related to the first period of the COVID-19 pandemic (March-June 2020), some recent studies on the application of content analysis of geolocalized tweets (Bashar et alii 2020, Punziano et alii 2020) have demonstrated the negative relation between the Coronavirus spread (with Northern Italy most affected) and the polarity of social narratives about the pandemic. In brief, the «resilient» social narrative of the most impacted regions has corresponded to a negative and worried emergency narrative of the less affected regions. In relation to epidemiological data, the second phase of the pandemic (also referred to as the «second wave», in autumn 2020) has been very different from the first (ISSa 2020). The severity of emergency, without considering questions about the reliability of the first wave data (Istat 2020a), has been more relevant and homogeneous across Italy. The study questions whether there are differences between the geography of contagion and that of the narrative. Given the increasingly homogeneous spread of the virus, the assumption has been that the digital arena also has ended up showing a narrative more united on negative sentiments. The issue is addressed by analyzing a corpus of geolocalized tweets, extracted in the period from the new October lockdown to the partial and fragmented pre-Christmas reopenings in 2020. Following the application of a model combining text mining and GIS analysis, the most recurrent themes in social discourse on Twitter were mapped. This geography of emerging social narratives (COVID-Issues) compared with the geography of contagion spread (COVID-Spread) and the norms (COVID-Measures) allowed to detect the trend in the relationship of these three dimensions during the second emergence from COVID-19.