Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19

Abstract

Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its widespread, educational institutions closed their campuses immediately and academic activities are moved to e-learning platforms. The effectiveness of e-learning is a critical concern for both students and parents, specifically in terms of its suitability to students and teachers and its technical feasibility with respect to different social scenarios. Such concerns must be reviewed from several aspects before e-learning can be adopted at such a larger scale. This study endeavors to investigate the effectiveness of e-learning by analyzing the sentiments of people about e-learning. Due to the rise of social media as an important mode of communication recently, people’s views can be found on platforms such as Twitter, Instagram, Facebook, etc. This study uses a Twitter dataset containing 17,155 tweets about e-learning. Machine learning and deep learning approaches have shown their suitability, capability, and potential for image processing, object detection, and natural language processing tasks and text analysis is no exception. Machine learning approaches have been largely used both for annotation and text and sentiment analysis. Keeping in view the adequacy and efficacy of machine learning models, this study adopts TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet to analyze the polarity and subjectivity score of tweets’ text. Furthermore, bearing in mind the fact that machine learning models display high classification accuracy, various machine learning models have been used for sentiment classification. Two feature extraction techniques, TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) have been used to effectively build and evaluate the models. All the models have been evaluated in terms of various important performance metrics such as accuracy, precision, recall, and F1 score. The results reveal that the random forest and support vector machine classifier achieve the highest accuracy of 0.95 when used with Bow features. Performance comparison is carried out for results of TextBlob, VADER, and SentiWordNet, as well as classification results of machine learning models and deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional-LSTM). Additionally, topic modeling is performed to find the problems associated with e-learning which indicates that uncertainty of campus opening date, children’s disabilities to grasp online education, and lagging efficient networks for online education are the top three problems.

Blood cancer prediction using leukemia microarray gene data and hybrid logistic vector trees model

“…Following that, you should feed any features to your classifier to see what the “predicted” class is after you have obtained the hyperplane. This makes this algorithm particularly good for our purposes, though it can be used in a variety of contexts 28 , 29 KNN KNN is a basic model used in machine learning for regression and classification processing. The data is referred to as the class with the closest neighbors, and the technique uses the data to organize the current data means based on a distance attribute.…”

Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model

“…While the majority of studies covered sentiment analysis of COVID-19 tweets in general, some studies put focus on more specific topics, such as vaccination [33,34] or online education [35,36]. There is also a small number of studies that focused more on the comparison of different algorithms for sentiment classification of COVID-19 tweets than on their application and analysis of results.…”

Classification of movie reviews using term frequency-inverse document frequency and optimized machine learning algorithms

“…Identification of useful features from the data is an important step for the better training of machine learning classifiers. The formation of secondary features from the original features enhances the efficiency of machine learning algorithms ( Oghina et al, 2012 ; Mujahid et al, 2021 ). It is one of the critical factors to increase the accuracy of the learning algorithm and boost its performance.…”