Arabic sentiment analysis about online learning to mitigate covid-19

Manal Mostafa Ali

doi:10.1515/jisys-2020-0115

Abstract

Abstract The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, the National Research Council Canada (NRC) Word-Emotion Lexicon was examined to calculate the presence of the eight emotions at their emotional weight. Third, Information Gain (IG) is used as a filtering technique. Fourth, the latent reasons behind the negative sentiments were recognized and analyzed. Finally, different classification algorithms including Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) were examined. The experiments reveal that the proposed model performs well in analyzing the perception of people about coronavirus with a maximum accuracy of about 89.6% using SVM classifier. From a practical perspective, the method could be generalized to other topical domains, such as public health monitoring and crisis management. It would help public health officials identify the progression and peaks of concerns for a disease in space and time, which enables the implementation of appropriate preventive actions to mitigate these diseases.

Highlights

We employed 10-fold cross-validation for all experiments by dividing the dataset into 10-folds, whereby one fold is used for testing purposes and the remaining 9 folds are used for training purposes
The first experiment is represented by evaluating the performance of the proposed method using either Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR) or Support Vector Machine (SVM) classifier
There is no significant difference in the performance of LR, and MNB which respectively recorded 86.6% and 85.9% followed by KNN which gives 83.9%

Summary

Introduction

The contingency assessments of public health officials and other agencies such as the World Health Organization (WHO) advised social distancing as a primary precautionary measure to mitigate the pandemic [1]. Under such a disruption, educational sectors over the globe have been hardly hit by the outbreak of the pandemic, which has led to profound changes in the educational deliveries. As a response to this, Internet and online courses become the best solution [2, 3]. Shifting from physical classrooms to online ones has not been without problems. Some challenges faced by online teaching platforms proposed by [4]. There is a critical need for practice-ready studies about distance learning so that authorities can make data-driven decision making from the insights of social media sentiment mining in real-time

Methods

Results

Conclusion