Sentiment analysis of arabic tweets for road traffic congestion and event detection

Abstract

Road traffic congestion is one of the most significant problems in the world, especially in large cities. In Saudi Arabia, accidents and traffic jams have increased in many major roads due to the lack of public transportation, increasing number of vehicles, and an enormous number of pilgrim visitors all year round. Twitter has emerged as an important source of information on various topics including road traffic. A large number of tweets are posted every day by users who wish to inform their followers about traffic conditions. Moreover, big data processing technologies provide unprecedented data analysis opportunities for addressing transportation problems. In this paper, we introduce a methodology for preprocessing and analyzing traffic-related tweets in the Arabic language, particularly the Saudi dialect using a big data processing platform (SAP HANA). Furthermore, we propose a technique for sentiment classification using lexicon-based approach to understand driver’s feelings. We collect tweets from Jeddah and Makkah cities and identify the most congested roads in the cities. We also detect events of multiple types: accidents, roadworks, fire, weather conditions, and others. The causes for the congestion in the cities are also identified.