Analyzing Student Evaluations of Teaching in a Completely Online Environment

Almost all educational institutions have shifted their academic activities to digital platforms due to the recent COVID-19 epidemic. Because of this, it is very important to assess how well teachers are performing with this new way of online teaching. Educational Data Mining (EDM) is a new field that emerged from using data mining techniques to analyze educational data and making decision based on findings. EDM can be utilized to gain better understanding about students and their learning processes, assist teachers do their academic tasks, and make judgments about how to manage educational system. The primary objective of this study is to uncover the key factors that influence the quality of teaching in a virtual classroom environment. Data is gathered from the students' evaluation of teaching from computer science students of three online semesters at X University. In total, 27622 students participated in these survey. Weka, sentimental analysis, and word cloud generator are applied in the process of carrying out the research. The decision tree classifies the factors affecting the performance of the teachers, and we find that student-faculty relation is the most prominent factor for improving the teaching quality. The sentimental analysis reveals that around 78% of opinions are positive and "good” is the most frequently used word in the opinions. If the education system is moved online in the future, this research will help figure out what needs to be changed to improve teachers' overall performance and the quality of their teaching.

MACHINE LEARNING MODELS TO PREDICT COVID-19 VACCINATION INTENTION: AN INDIAN STUDY

Purpose: Covid 19 pandemic has taken the world by shock for last few years, and it has greatly impacted the livelihood of people across all walks of life and even the economies of many nations got greatly affected. Governments across the globe revived from the impact of covid-19 pandemic using many strategies and policies which were formulated under the guidance of the world health organization. One of the Prime weapons which helped the governments and public against covid -19 is vaccination. This research which was conducted August 2021 was done to understand the perception of the public towards the covid 19 vaccination and to predict the public intention to take up covid -19 vaccination using the health belief model constructs. Theoretical framework:The Study has used the variables of the health belief model namely the perceived severity, perceived susceptibility, Perceived Benefits, Cues to action and other socio-demographic variables to predict the intent of the respondents towards taking Covid-19 vaccination. Design/methodology/approach: Data was collected using a self-administered online questionnaire distributed to the respondents from Tamil Nadu, India who are above 18 years of age. Machine Learning Algorithms like Logistic Regression, Artificial Neural Networks were used to predict the public intent to take up covid 19 vaccination. Findings: From the Analysis of Logistic Regression and Artificial Neural Network, it was found that Health Belief Model Constructs Perceived Barriers, Perceived Benefits and Cues to action, were significant factors that affect the public intention to vaccinate. Research, Practical & Social implications:Findings of the research will help the government, stake holders to understand the factors impacting the respondent's intent to covid-19 vaccination which will guide them to plan better strategies for future vaccination drives Originality/value:The Study has used to two different machine learning algorithms to compare and corroborate the research findings and in turn identifying the significant predictors of covid-19 vaccination intent © 2022 AOS-Estratagia and Inovacao. All rights reserved.

Sentimental Analysis of COVID-19 Vaccine Tweets Using BERT+NBSVM

The development of the vaccine for the control of COVID-19 is the need of hour. The immunity against coronavirus highly depends upon the vaccine distribution. Unfortunately, vaccine hesitancy seems to be another big challenge worldwide. Therefore, it is necessary to analysis and figure out the public opinion about COVID-19 vaccines. In this era of social media, people use such platforms and post about their opinion, reviews etc. In this research, we proposed BERT+NBSVM model for the sentimental analysis of COVID-19 vaccines tweets. The polarity of the tweets was found using TextBlob(). The proposed BERT+NBSVM outperformed other models and achieved 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people's attitudes towards the vaccines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.

E-learning course recommendation based on sentiment analysis using hybrid Elman similarity

Online learning is also referred to as E-learning which has gained huge attention and attracted most people during the COVID-19 lockdowns. Due to the excess of online information, users face severe challenges and difficulties realizing the best course that is being competitive in the global market. Therefore, it is necessary to develop an online recommendation system that supports the users in selecting the finest course with E-learning. Thus, the proposed work develops a robust RS model using different approaches. Initially, the pre-processing stage is performed to reduce the presented noise in the website data. Then, the feature extraction stage is done to extract the needed features using Improved TF-IDF, W2V (Word 2 Vector), and Hybrid N-gram. Finally, Elman Minimal Redundancy Maximum Relevance and Enhanced Aquila Optimization (EMRMR_EAO) model is proposed to provide Robust course recommendations. In this work, the ERNN method is used to classify the sentiments based on the similarity measure of the MRMR model. The top course recommendation is afforded depending on the similarity scores like Jaccard similarity, cosine similarity and euclidean similarity. Also, the loss function in the classifier is reduced by optimizing the weight parameters using the EAO approach. The performance analysis shows that the proposed recommendation model obtains improved results in terms of accuracy of 99.98%, recall of 99.81%, precision of 99.65%, and F-measure of 99.95%. The comparative analysis exhibit that the proposed EMRMR_EAO model attains better performance than the other existing works in the literature. © 2022 Elsevier B.V.

Topic Modelling and Sentimental Analysis of Students' Reviews

Globally, educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 pandemic. The fundamental concern has been the continuance of education. As a result, several novel solutions have been developed to address technical and pedagogical issues. However, these were not the only difficulties that students faced. The implemented solutions involved the operation of the educational process with less regard for students' changing circumstances, which obliged them to study from home. Students should be asked to provide a full list of their concerns. As a result, student reflections, including those from Saudi Arabia, have been analysed to identify obstacles encountered during the COVID- 19 pandemic. However, most of the analyses relied on closed-ended questions, which limited student involvement. To delve into students' responses, this study used open-ended questions, a qualitative method (content analysis), a quantitative method (topic modelling), and a sentimental analysis. This study also looked at students' emotional states during and after the COVID-19 pandemic. In terms of determining trends in students' input, the results showed that quantitative and qualitative methods produced similar outcomes. Students had unfavourable sentiments about studying during COVID-19 and positive sentiments about the face-to-face study. Furthermore, topic modelling has revealed that the majority of difficulties are more related to the environment (home) and social life. Students were less accepting of online learning. As a result, it is possible to conclude that face-to-face study still attracts students and provides benefits that online study cannot, such as social interaction and effective eye-to-eye communication. © 2023 Tech Science Press. All rights reserved.

Analysis Of COVID-19 Effects On Wellbeing - Study Of Reddit Posts Using Natural Language Processing Techniques

The COVID-19 pandemic continues to negatively impact people's mental health worldwide. Due to the rise in unemployment, loss of income, and lack of social interaction, people are now more likely to feel lonely, go on fewer outings, and dread the unexpected nature of viral transmission. Meanwhile, Public Health authorities are interested in monitoring people's mental and emotional well-being. In this paper, natural language processing is used to analyze human sentiments concerning the COVID-19 pandemic that has been dangerously affecting individuals' mental and physical well-being for more than two years now. Even though several waves of COVID-19 have passed, of which the first and third waves i.e., the initial pandemic period from 20th March 2020 and the rise of the Delta variant from January 2020 had the most impact on the mental health of individuals, this is further evident by the results of this paper. This research focuses on how severely this virus has affected people's mental health and emotions. After processing the data i.e., cleaning, formatting, and removing irregularities from the data, feature engineering models are applied to acquire the results. The results through VADER (valence-aware dictionary and sentiment reasoning) indicate an increase in overall negative sentiments between two mentioned periods. Additionally, the NRC-EIL (National Research Council of Canada - Emotion Intensity Lexicon) analysis showed that 'fear' and 'sadness' occurred during those times. © 2022 IEEE.

Mean Silhouette Genetic and Elman Deep Recurrent Network based Sentimental Analysis for Twitter Social Media Healthcare Data

Employment of social media is becoming all-pervasive and disease analogous communities are organizing online, together with communities of attentiveness encompassing health care domain. Despite Facebook being the most sought out social media platform, utilization of supplementary social media platform like Twitter is increasing. To be more specific, in recent days patients with COVID and diabetes commenced to gather and take active participations in online discussions about diabetes and COVID on Twitter, engross in communication and sharing virtually and perceive peer support online. In this work a method called, Mean Silhouette-based Genetic and Elman Deep Sentiment Analysis (MSG-EDSA) is proposed. Elman Deep Recurrent Network Sentiment Classification is applied to the selected features, to classify the tweets in an accurate and timely manner. The tweets are finally classified as extremely positive, positive, extremely negative, negative or neutral from the tweets obtained via different users. The proposed MSG-EDSA is experimented with using the diabetes and COVID real-time datasets from social media platforms to analyze healthcare sentimental analysis. The parameters like, precision, recall, accuracy and error rate are selected to analyze the performance against the state-of-the-art sentimental analysis methods. Copyright © 2022, Anka Publishers. All rights reserved.

Topic Modelling and Sentimental Analysis of Students' Reviews

Globally, educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 pandemic. The fundamental concern has been the continuance of education. As a result, several novel solutions have been developed to address technical and pedagogical issues. However, these were not the only difficulties that students faced. The implemented solutions involved the operation of the educational process with less regard for students' changing circumstances, which obliged them to study from home. Students should be asked to provide a full list of their concerns. As a result, student reflections, including those from Saudi Arabia, have been analysed to identify obstacles encountered during the COVID- 19 pandemic. However, most of the analyses relied on closed-ended questions, which limited student involvement. To delve into students' responses, this study used open-ended questions, a qualitative method (content analysis), a quantitative method (topic modelling), and a sentimental analysis. This study also looked at students' emotional states during and after the COVID-19 pandemic. In terms of determining trends in students' input, the results showed that quantitative and qualitative methods produced similar outcomes. Students had unfavourable sentiments about studying during COVID-19 and positive sentiments about the face-to-face study. Furthermore, topic modelling has revealed that the majority of difficulties are more related to the environment (home) and social life. Students were less accepting of online learning. As a result, it is possible to conclude that face-to-face study still attracts students and provides benefits that online study cannot, such as social interaction and effective eye-to-eye communication. © 2023 Tech Science Press. All rights reserved.

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.

E-learning course recommendation based on sentiment analysis using hybrid Elman similarity

Online learning is also referred to as E-learning which has gained huge attention and attracted most people during the COVID-19 lockdowns. Due to the excess of online information, users face severe challenges and difficulties realizing the best course that is being competitive in the global market. Therefore, it is necessary to develop an online recommendation system that supports the users in selecting the finest course with E-learning. Thus, the proposed work develops a robust RS model using different approaches. Initially, the pre-processing stage is performed to reduce the presented noise in the website data. Then, the feature extraction stage is done to extract the needed features using Improved TF-IDF, W2V (Word 2 Vector), and Hybrid N-gram. Finally, Elman Minimal Redundancy Maximum Relevance and Enhanced Aquila Optimization (EMRMR_EAO) model is proposed to provide Robust course recommendations. In this work, the ERNN method is used to classify the sentiments based on the similarity measure of the MRMR model. The top course recommendation is afforded depending on the similarity scores like Jaccard similarity, cosine similarity and euclidean similarity. Also, the loss function in the classifier is reduced by optimizing the weight parameters using the EAO approach. The performance analysis shows that the proposed recommendation model obtains improved results in terms of accuracy of 99.98%, recall of 99.81%, precision of 99.65%, and F-measure of 99.95%. The comparative analysis exhibit that the proposed EMRMR_EAO model attains better performance than the other existing works in the literature.

Using High Performance Approaches to Covid-19 Vaccines Sentiment Analysis

Coronovirus has emerged as challenge for the whole mankind causing illness worldwide. To eradicate the disease, global efforts are put increasing to develop its vaccine. In order to achieve the immunity against the virus, wide provision of vaccine is necessary. To make sure the distribution of vaccines, the sentiments of people for vaccines must be analyzed. Now-a-days, people share their thoughts, feelings and feedback about anything they experience on social media platforms. In this study, high performance approaches have been used for the analysis of the sentiments of people about vaccines. In this study, we have used the freely available data and applied pre-processing over it. We found out the polarity values of the tweets using TextBlob() function of Python and drew the wordclouds for positive, negative and neutral tweets. We used BERT model for understanding the people's feelings and feedback about vaccines. The model evaluation was performed using precision, recall and F measure. The BERT model achieved achieved 55 % & 54 % precision, 69 % & 85 % recall and 58 % & 64 % F score for positive class and negative class respectively. Therefore, the use of artificial intelligence in social media analysis produce fruitful results while determining the people's attitude towards ant new trend, topic and any emergency situation. These methods helps to grow the vaccines campaigns timely by solving the people's concerns about vaccines.

Sentimental Analysis on Online Education Using Machine Learning Models

Sentimental analysis is a simple natural language processing technique for classifying and identifying the sentiments and views represented in a source text. Corona pandemic has shifted the focus of education from traditional classrooms to online classes. Students’ mental and psychological states alter as a result of this transition. Sentimental study of the opinions of online education students can aid in understanding the students’ learning conditions. During the corona pandemic, only, students enrolled in online classes were surveyed. Only, students who are in college for pre-graduation, graduation, or post-graduation were used in this study. To grasp the pupils’ feelings, machine learning models were developed. Using the dataset, we were able to identify and visualize the students’ feelings. Students’ favorable, negative, and neutral opinions can be successfully classified using machine learning algorithms. The Naive Bayes method is the most accurate method identified. Logistic regression, support vector machine, decision tree, and random forest these algorithms also gave comparatively good accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Depression Detection from Social Site using Machine Learning and Deep Learning

As COVID-19 crisis is settling down in countries, whether or not a person has been affected personally by the disease, he fights with issues such as anxiety, panic attacks, grief, low mood, and many other psychotic disorders. Mental fitness is one of the major strengths in the development of the individual. Development of social sites turns out to be one platform where the person feels free to vent out their thoughts and to easily interact with people. Extracting useful information from those posts is a part of sentimental analysis, which is the technique of machine learning that helps to know the mental condition of the individual. In this paper, various machine learning algorithms such as random forest, Naive Bayes, decision tree, multilayer perceptron, maximum entropy, KNN, gradient boosted decision tree, adaptive boosting, bagged logistic regression, tree ensemble model, Liblinear, convolutional neural network, and long short-term memory are applied on the dataset, and different mathematical scales such as accuracy, precision, recall, and F1 score concluded that bagged logistic regression has given the better accuracy results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Sentimental Analysis of Twitter Data on Online Learning During Unlock Phase of COVID-19

The coronavirus-induced lockdown has brought everyone’s life to a standstill and negatively impacted sentiments worldwide. It has also made online learning a compulsion for most students. The world is now following work from home and the e-learning boom. Almost all the schools and colleges have leveraged distance learning, thus continuing education from home and learning processes. Thus, online learning has a significant effect on education and has become a viable option for offline classes. The new norm has opened doors for blended learning, which will likely stay in the future. Our study analyses the sentiments of students on online learning. The proposed technique has been used to analyze tweets are subjectivity and polarity and later performed statistical analysis on cleaned tweets to know students’ sentiments on online learning. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Sentimental Analysis of Fears, Psychological Disorders and Health Issues Through NVIVO During Second Wave of Covid-19

The chapter discusses about the sentimental analysis of fears, psychological disorders and health issues of the individuals. The qualitative data of 42 millennials working in different sectors has been collected to analyze the findings. The major findings of the chapter dealt with how the millennials are facing the challenges of several psychological disorder, fears in order to fulfill their jobs and duties during Covid-19, which impact their physical health as well. The study provides a result discussion with the help of word cloud and sentimental analysis, thematic analysis of millennials through NVIVO-12. The chapter concludes that this pandemic creates a window for discussing the psychological fears and mental health issues openly in Indian context. The chapter provides a future scope for the researcher to do the quantitative analysis for the same variables. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Sentimental Analysis of Streaming COVID-19 Twitter Data on Spark-Based Framework

Social media has become an inevitable part of human’s daily life enabling people to express their opinion, sentiments, and ideologies. During this COVID-19 pandemic when the whole world went into a lockdown situation, Twitter served as an outlet for people to express their emotions. This work proposes streaming the real-time Twitter data on COVID-19 using Twitter API and handling the streaming big data using the Apache Spark framework. Here the fake account detection to detect the non-legit accounts present in the streamed data was accomplished by the proposed feature-based algorithm which attain overall accuracy of 98.74%. This constructed fake account detection model filters out the genuine accounts from the API streamed Twitter data. Sentimental analysis on these genuine Twitter accounts is performed by modifying the Natural Language Processing (NLP) state-of-art algorithm called Bidirectional Encoder Representations from Transformers (BERT). The proposed method achieved 88.30% of classification accuracy rate by concatenation of the pooled NN layer with the influential feature. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Twitter based sentimental analysis of Covid-19 observations.

The emergence of social media has provided people with the opportunity to express their feelings and thoughts about everything and everything in their lives. There is a massive amount of textual stuff available, and approaches are required to make meaningful use of the information provided by isolating and evaluating the different types of text. Sentimental Analysis is a method of obtaining a human being's point of view through mining his or her emotions. The entire world is sharing their thoughts on social media on the Corona Pandemic that is now underway. This research presents an analysis of attitudes in order to determine whether or not people are optimistic in the face of a difficult circumstance. The technique of polarity is employed by the paper in order to determine if an opinion is positive, negative, or nonpartisan [1]. In order to determine the polarity, the following three major keywords are used: "COVID", "Corona virus," and "COVID-19."

Sentimental Analysis Based on Cold-Start Recommendation with Deep Neural Learning (SACNN): A Novel Approach for Travel Recommendation in Pandemic

Travelling is always being a usual thing where people travel for particular reasons such as business meetings, vacation, medical emergencies, and get-together parties, etc. But travelling in the covid-19 situation has been a concern, where there are lots of restrictions are allotted in various cities to control the pandemic situation. To control the pandemic among the user during travelling and to obtain easy information access 'travel recommendation correlated with social media is used. In the proposed system the system analyzes the user's social media accounts to gather information and updates the travel history. Hereby, when a new user surfs for any travel updates the server undergoes a validation process and suggests accordingly. For recommendation purposes, the proposed system introduces a new novel mechanism named 'Sentimental Analysis Based Cold-start recommendation with Deep Neural Learning (SACNN)'. In this method, all the recent travel and covid-19 related details are stored and saved for user check. Further, the system for security enables a fake identification classifier to detect fake information in social media. The proposed theory will provide better accuracy rate than the existing other performances.

A Study on Predicting Customer Willingness to Order Food Online during Covid-19 Pandemic Using Machine Learning Algorithms

Online food delivery has become the one of the prominent services during COVID-19 pandemic. After facing deceleration in early COVID-19 phase, online food delivery is slowly gaining momentum in India due to relaxations given by the government and support of the consumers. Online food delivery services need an improved understanding of the complexities of customer behavior which have shifted during this health crisis period of COVID-19 pandemic. The Study is undertaken to predict the customer willingness to order food using online services aftermath of COVID-19 pandemic using Machine Learning algorithms. Primary data collection is done through online survey distributed among public. 415 responses were received out of which 369 people prefer to order through online food delivery services. Using different machine learning models, it is inferred that the Affective and instrumental belief, Perceived benefits (variables of health belief model) are the significant predictors of the customers willingness to order food online. Demographic variables like hours utilized in mobile, frequency of ordering during COVID, Convenience of using food delivery application, number of members in family, age, education qualification and occupation are also found to be significant in determining order opinion. © 2021 IEEE.