Detecting Epidemic Diseases Using Sentiment Analysis of Arabic Tweets

Qanita Bani Baker (Jordan University of Science and Technology, Jordan)

Farah Shatnawi (Jordan University of Science and Technology, Jordan)

Saif Rawashdeh (Jordan University of Science and Technology, Jordan)

Mohammad Al-Smadi (Jordan University of Science and Technology, Jordan)

Yaser Jararweh (Jordan University of Science and Technology, Jordan)

Abstract: Opinion mining is an important step towards facilitating information in health data. Several studies have demonstrated the possibility of tracking diseases using public tweets. However, most studies were applied to English language tweets. Influenza is currently one of the world's greatest infectious disease challenges. In this study, a new approach is proposed in order to detect Influenza using machine learning techniques from Arabic tweets in Arab countries. This paper is the first study of epidemic diseases based on Arabic language tweets. In this work, we have collected, labeled, filtered and analyzed the influenza-related tweets written in the Arabic language. Several classifiers were used to measure the quality and the performance of the approach, which are: Naive Bayes, Support Vector Machines, Decision Trees, and K-Nearest Neighbor. The classifiers which achieved the best accuracy results for the three experiments were: Naïve Bayes with 89.06%, and K-Nearest Neighbor with 86.43%, respectively.

Keywords: Arabic tweets, Twitter, data mining, infectious diseases, influenza, machine learning, sentiment analysis

Categories: I.2, J.3, L.2