Analyzing Indian general public's perspective on anxiety, stress and trauma during Covid-19 - A machine learning study of 840,000 tweets

On the use of sentiment analysis for linguistics research. Observations on sentiment polarity and the use of the progressive in Italian

This article offers a conceptual and methodological contribution to linguistics by exploring the potential value of using sentiment analysis (SA) for research in this field. Firstly, it discusses the limitations and advantages of using SA for linguistics research including the wider epistemological implications of its application outside of its original conception as a product reviews analysis tool. Methodologically, it tests its applicability against an established linguistic case: the correlation between subjective attitudes such as surprise, irritation and discontent and the use of the progressive. The language example is Italian for which this function of the progressive form has not been analyzed yet. The analysis applies FEEL-IT, a state-of-the-art transformer-based machine learning model for emotion and sentiment classification in Italian on language samples from various sources as collected in Evalita-2014 (238,556 words). The results show statistically significant correlations between negative subjective attitudes and the use of the progressive in line with previous accounts in other languages. The article concludes with a few additional propositions for practitioners and researchers using SA.

COVID-19 vaccination perception and outcome: society sentiment analysis on twitter data in India

This study examines the perceptions and results of COVID-19 immunization using sentiment analysis of Twitter data from India. The tweets were collected from January 2021 to March 2023 using relevant hashtags and keywords. The dataset was pre-processed and cleaned before conducting sentiment analysis using Natural Language Processing techniques. Our results show that the overall sentiment toward COVID-19 vaccination in India has been positive, with a majority of tweets expressing support for vaccination and encouraging others to get vaccinated. However, we also identified some negative sentiments related to vaccine hesitancy, side effects, and mistrust in the government and pharmaceutical companies. We further analyzed the sentiment based on demographic factors such as gender, age, and location. The analysis revealed that the sentiment varied across different demographics, with some groups expressing more positive or negative sentiments than others. This study provides insights into the perception and outcomes of COVID-19 vaccination in India and highlights the need for targeted communication strategies to address vaccine hesitancy and increase vaccine uptake in specific demographics.

Agile Methodologies Applied to the Development of Internet of Things (IoT)-Based Systems: A Review

Throughout the evolution of software systems, empirical methodologies have been used in their development process, even in the Internet of Things (IoT) paradigm, to develop IoT-based systems (IoTS). In this paper, we review the fundamentals included in the manifesto for agile software development, especially in the Scrum methodology, to determine its use and role in IoTS development. Initially, 4303 documents were retrieved, a number that was reduced to 186 after applying automatic filters and by the relevance of their titles. After analysing their contents, only 60 documents were considered. Of these, 38 documents present the development of an IoTS using some methodology, 8 present methodologies focused on the construction of IoTS software, and 14 present methodologies close to the systems life cycle (SLC). Finally, only one methodology can be considered SLC-compliant. Out of 38 papers presenting the development of some IoTS following a methodology for traditional information systems (ISs), 42.1% have used Scrum as the only methodology, while 10.5% have used Scrum combined with other methodologies, such as eXtreme Programming (XP), Kanban and Rapid Prototyping. In the analysis presented herein, the existing methodologies for developing IoTSs have been grouped according to the different approaches on which they are based, such as agile, modelling, and service oriented. This study also analyses whether the different proposals consider the standard stages of the development process or not: planning and requirements gathering, solution analysis, solution design, solution coding and unit testing (construction), integration and testing (implementation), and operation and maintenance. In addition, we include a review of the automated frameworks, platforms, and tools used in the methodologies analysed to improve the development of IoTSs and the design of their underlying architectures. To conclude, the main contribution of this work is a review for IoTS researchers and developers regarding existing methodologies, frameworks, platforms, tools, and guidelines for the development of IoTSs, with a deep analysis framed within international standards dictated for this purpose.

Sentimental and spatial analysis of COVID-19 vaccines tweets

The world has to face health concerns due to huge spread of COVID. For this reason, the development of vaccine is the need of hour. The higher vaccine distribution, the higher the immunity against coronavirus. Therefore, there is a need to analyse the people's sentiment for the vaccine campaign. Today, social media is the rich source of data where people share their opinions and experiences by their posts, comments or tweets. In this study, we have used the twitter data of vaccines of COVID and analysed them using methods of artificial intelligence and geo-spatial methods. We found the polarity of the tweets using the TextBlob() function and categorized them. Then, we designed the word clouds and classified the sentiments using the BERT model. We then performed the geo-coding and visualized the feature points over the world map. We found the correlation between the feature points geographically and then applied hotspot analysis and kernel density estimation to highlight the regions of positive, negative or neutral sentiments. We used precision, recall and F score to evaluate our model and compare our results with the state-of-the-art methods. The results showed that our model achieved 55% & 54% precision, 69% & 85% recall and 58% & 64% F score for positive class and negative class respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people's attitudes towards the vaccines.

Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts Using Natural Language Processing, to Understand People's Perspectives Regarding COVID-19 Booster Vaccine Shots in India: Crucial to Expanding Vaccination Coverage

This study analyzed perceptions of Indians regarding COVID-19 booster dose vaccines using natural language processing techniques, particularly, sentiment analysis and topic modeling. We analyzed tweets generated by Indian citizens for this study. In late July 2022, the Indian government hastened the process of COVID-19 booster dose vaccinations. Understanding the emotions and concerns of the citizens regarding the health policy being implemented will assist the government, health policy officials, and policymakers implement the policy efficiently so that desired results can be achieved. Seventy-six thousand nine hundred seventy-nine tweets were used for this study. The sentiment analysis study revealed that out of those 76,979 tweets, more than half (n = 40,719 tweets (52.8%) had negative sentiments, 24,242 tweets (31.5%) had neutral sentiments, and 12,018 tweets (15.6%) had positive sentiments. Social media posts by Indians on the COVID-19 booster doses have focused on the feelings that younger people do not need vaccines and that vaccinations are unhealthy.

Vaccine hesitancy for coronavirus SARS-CoV-2 in Varanasi India

With the rollout of the world's largest vaccine drive for SARS-CoV-2 by the Government of India on January 16 2021, India had targeted to vaccinate its entire population by the end of 2021. Struggling with vaccine procurement and production earlier, India overcome these hurdles, but the Indian population still did not seem to be mobilizing swiftly toward vaccination centers. The severe second wave has slowed the vaccination pace and was also one of the major contributing factors to vaccine hesitancy. To understand the nature of vaccine hesitancy and its underlying factors, we conducted extensive online and offline surveys in Varanasi and adjoining regions using structured questions. Most respondents were students (0.633). However, respondents from other occupations, such as government officials (0.10), have also participated in the study. Interestingly, most people (0.75) relied on fake news and did not take COVID-19 seriously. Most importantly, we noticed that a substantial proportion of respondents (relative frequency 0.151; mean age 24.8 years) reported that they were still not interested in vaccination. We observed a significant association between vaccine hesitancy and socioeconomic status (χ2 = 307.6, p < 0.001). However, we failed to detect any association between vaccine hesitancy and gender (χ2 = 0.007, p > 0.5). People who have neither been vaccinated nor have ever been infected may become the medium for spreading the virus and creating new variants, which may lead to the vaccine-resistant variant. We expect this extensive survey to help the Government upgrade their vaccination policies for COVID-19 in North India.

Mining Social Media Data to Predict COVID-19 Case Counts

The unpredictability and unknowns surrounding the ongoing coronavirus disease (COVID-19) pandemic have led to an unprecedented consequence taking a heavy toll on the lives and economies of all countries. There have been efforts to predict COVID-19 case counts (CCC) using epidemiological data and numerical tokens online, which may allow early preventive measures to slow the spread of the disease. In this paper, we use state-of-the-art natural language processing (NLP) algorithms to numerically encode COVID-19 related tweets originated from eight cities in the United States and predict city-specific CCC up to eight days in the future. A city-embedding is proposed to obtain a time series representation of daily tweets posted from a city, which is then used to predict case counts using a custom long-short term memory (LSTM) model. The universal sentence encoder yields the best normalized root mean squared error (NRMSE) 0.090 (0.039), averaged across all cities in predicting CCC six days in the future. The R ² scores in predicting CCC are more than 0.70 and often over 0.8, which suggests a strong correlation between the actual and our model predicted CCC values. Our analyses show that the NRMSE and R ² scores are consistently robust across different cities and different numbers of time steps in time series data. Results show that the LSTM model can learn the mapping between the NLP-encoded tweet semantics and the case counts, which infers that social media text can be directly mined to identify the future course of the pandemic.

The Level of Fear Experienced by the Individuals and their Applications to Health Institutions during the Covid-19 Pandemic

AIM

The study aims to investigate the effect of the level of fear experienced by individuals during the COVID-19 pandemic on their application to health institutions.

METHOD

This descriptive study was conducted between July and September 2020 with the participants who met the inclusion criteria in Turkey. When the mean COVID-19 Fear Scale score was considered and the standard deviation values ​​were taken as 18.83 ± 6.01, the sample size was determined as 98 individuals, with 95% confidence level, 90% test power, and 0.331 effect size. With the snowball sampling method, the study was carried out with 577 people who filled out the Google form. The Personal Information Form and the COVID-19 Fear Scale were used as data collection tools. The data were analyzed with SPSS 20.0. Descriptive statistics, correlation, Mann-Whitney U (U), and Kruskal-Wallis tests were performed to analyze the data. Ethics committee approval was obtained prior to the study.

RESULTS

The mean age of the participants was 32.06 ± 11.25 (min 18-max 71); 77.8% were female; 66% were university graduates, and 54.9% were single. The total mean score of the participants from the COVID-19 Fear Scale was determined as 16.84 ± 5.68 (min 7-max 34), which points to moderate level of fear. The COVID-19 Fear Scale scores of the female participants, the participants with high income, and those living with their families were found to be higher (p < 0.05). The COVID-19 Fear Scale scores were found to be higher in those who sleep less than 7 hours a day, who have a psychological disorder, who applied to health institutions during the coronavirus process, and who postponed their application to health institutions in an emergency due to the fear of infection (p < 0.05).

CONCLUSION

It has been determined that during the coronavirus process, 21.5% of individuals attend in person to health institutions and 40.7% of individuals attend in person to health institutions in emergencies. It was found that the level of fear was higher in the participants who applied to health institutions during the pandemic. The participants who postponed their application to health institutions in emergencies due to the fear of infection were found to have higher levels of fear.

Mental Health Interest and Its Prediction during the COVID-19 Pandemic Using Google Trends

This study aimed to analyze and predict interest in mental health-related queries created in Google Trends (GT) during the COVID-19 pandemic. The Google Trends tool collected data on the Google search engine interest and provided real-time surveillance. Five key phrases: “depression”, “insomnia”, ”loneliness”, “psychologist”, and “psychiatrist”, were studied for the period from 25 September 2016 to 19 September 2021. The predictions for the upcoming trend were carried out for the period from September 2021 to September 2023 and were estimated by a hybrid five-component model. The results show a decrease of interest in the search queries “depression” and “loneliness” by 15.3% and 7.2%, respectively. Compared to the period under review, an increase of 5.2% in “insomnia” expression and 8.4% in the “psychiatrist” phrase were predicted. The expression “psychologist” is expected to show an almost unchanged interest. The upcoming changes in the expressions connected with mental health might be explained by vaccination and the gradual removal of social distancing rules. Finally, the analysis of GT can provide a timely insight into the mental health interest of a population and give a forecast for a short period trend.

Understanding Customers’ Transport Services with Topic Clustering and Sentiment Analysis

The recent increase in user interaction with social media has completely changed the way customers communicate their opinions, questions, and concerns to brands. For this reason, many companies have established on the top of their agendas the necessity of analyzing the high amounts of user-generated content data in social networks. These analyses are helping brands to understand their customers’ experiences as well as for maintaining a competitive advantage in the sector. Due to this fact, this study aims to analyze and characterize the public opinions from the messages posted by Twitter users while addressing customer services. For this purpose, this study carried out a content analysis of a customer service platform. We extracted the general users’ viewpoints and sentiments of each of the discussed topics by using a wide range of techniques, such as topic modeling, document clustering, and opinion mining algorithms. For training these systems and drawing conclusions, a dataset containing tweets from the English-speaking customers addressing the @Uber_Support platform during the year 2020 has been used.

Fear of COVID-19 scale: Psychometric properties, reliability and validity in Egyptian population

BACKGROUND AND AIMS

The primary purpose of this study is to examine the psychometric qualities of the Fear of COVID-19 Scale (FCV-19S) in a sample of Egyptian college students. The researchers also aim at exploring the construct validity further through examining the relationship between FCV-19S, wellbeing and life satisfaction in Egyptian universities context. The current study aims to evaluate the psychometric properties of the Arabic version of the Fear of COVID-19 scale among Egyptian population.

METHODS

The FCV-19S is translated and validated in Egyptian context. The forward backward translation method is used to translate the English version of the survey into Arabic. The sample is comprised of 1832 Egyptian participants, who have conducted an online survey based on the Arabic versions of FCV-19S.

RESULTS

The Cronbach α value for the Egyptian FCV-19S is 0.87, indicating a good internal reliability. The results of the confirmatory factor analysis show that the unidimensional factor structure of the FCV-19S has fitted well with the data. The FCV-19S is significantly correlated with the seven-item survey. Moreover, the results show a significant negative relationship between Fear of COVID-19 and both wellbeing and life satisfaction (r = -0.42, p < 0.001; r = -0.24, p < 0.01.), respectively.

CONCLUSION

The study supports the one factor model of FCV-19S scale in Egyptian context. The Arabic scale version in the Egyptian context shows excellent reliability and validity. This ensures a good measure that can be accepted for both academia and practitioners for analyzing ill-effects of pandemic impacts and, thereby, reducing them.

Indian citizen's perspective about side effects of COVID-19 vaccine - A machine learning study

BACKGROUND AND AIMS

Ever since the vaccination drive for COVID-19 has started in India, the citizens have been sharing their views on social media about it. The present study examines the attitude of Indian citizens towards the side effects of the COVID-19 vaccine.

METHODS

Social media posts were used for this research. Using Python, we have collected social media posts of Indians focusing on side effects of COVID -19 vaccines. In study one, sentimental analysis was done to find overall attitude of Indian citizens towards the side effects of COVID-19 vaccine and in study two, topic modeling done to analyze the major side effects voiced out by the citizens after taking COVID-19 vaccine.

RESULTS

The studies conducted have revealed that nearly 78.5% of tweets posted by Indian citizens about the side effects of the COVID-19 vaccine were either in neutral or positive sentiments. Our topic modeling studies have found that fear of efficiency in the workplace and the fear of death as the prime two issues that contributes Indian citizens to have negative sentiment about the side effects of the COVID-19 vaccine.

CONCLUSION

While it is important for the Indian government to actively encourage its citizens to have vaccine, it is also important to help the citizens understand the important of the vaccination program. The best way to educate citizens regarding the positive aspect of the vaccination program is by addressing the fears, Indian citizens have voiced in their social media post about the COVID-19 vaccines.