Sentiment Analysis and Topic Modeling on Arabic Twitter Data during Covid-19 Pandemic
Nassera Habbat, Houda Anoun, Larbi Hassouni
Abstract
Twitter Sentiment Analysis is the task of detecting opinions and sentiments in tweets using different algorithms. In our research work, we conducted a study to analyze and compare different Algorithms of Machine Learning (MLAs) for the classification task, and hence we collected 37 875 Moroccan tweets, during the COVID-19 pandemic, from 01 March 2020 to 28 June 2020. The analysis was done using six classification algorithms (Naive Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest classifier) and considering Accuracy, Recall, Precision, and F-Score as evaluation parameters. Then we applied topic modeling over the three classified tweets categories (negative, positive, and neutral) using Latent Dirichlet Allocation (LDA) which is among the most effective approaches to extract discussed topics. As result, the logistic regression classifier gave the best predictions of sentiments with an accuracy of 68.80%.