Performance Evaluation in Arabic Sentiment Analysis during the Covid-19 Pandemic

Abstract

This paper classifies sentiment analysis in Arabic language and mining sentiment in relation to the COVID-19 pandemic in the period (2019 -2021). Three large data sets are collected from tweets, hotel and restaurant reviews for building the proposed sentiment analysis model. We compared eight machine learning algorithms, Multinomial Naïve Bayes (MNB), Bernoulli Naïve Bayes (BNB), Decision Tree (DT), K-nearest neighbour classifier (KNN), Support Vector Machines (SVM), Linear Support Vector Classifier (LSVC), Random Forest Classifier (RFC) and Stochastic Gradient Descent Classifier (SGD) on three cases: n-gram unigram, bigram, and trigram for each algorithm. The performance evaluations are compared according to precision, recall, and Fmeasure.The polarity prediction results in sentiment analysis models were achieved by linear SVC using hotel dataset with bigram case, with the accuracy of 0.966, precision of 0.967, recall of 0.966 and F-measure of 0.966 . The rest algorithms give normal execution on all datasets. It may very well be reasoned that the AI calculations need the right morphological components to upgrade the classification exactness when managing various words that assume various parts in the sentence with a similar letter.

Sentiment analysis and emotion detection of post-COVID educational Tweets: Jordan case

“…As a result of the impact of such a pandemic on the education system, these nations pushed on improving broadcast teaching, virtual class infrastructures and online teaching. Sakr et al (2021). Where according to the study conducted by Aristovnik et al (2020) which undertaken nearly 30,000 sample students tweets from 62 countries analyzed using the Naive-based classifier, concluded that the lower living standard students with financial problems were not satisfied with their academic life as the pandemic affected their life more.…”