Social Media Sentiment Analysis: The Hajj Tweets Case Study

Abstract

About forty five percent of the world's population use social networks, thinking of using these platforms seemed to find people's opinions and feelings on various topics. Companies that offer their services and products to customers focus on the subject for future improvement. Thus, serious thinking began to analyze the views of people across different social platforms and also to develop the best ways to analyze these views. In this study, we focused on finding the best way for sentiment analysis by using a series of Hajj-related tweets, which is one of the most important rituals performed by Muslims, where the companies responsible for the pilgrimage season seek to complete the season in best way every year. We used the Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) as supervised algorithms for machine-learning approach and Text Blob analyzer for lexicon-based approach. Finding shows that, machine learning techniques worked better than the lexicon approach in the classification and analysis of Hajj related tweets. Even the limited availability of Hajj tweets corpus dataset, SVM reaches the best accuracy which was 84%.