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Sentiment Analysis of Arabic Tweets for Road Traffic Congestion and Event Detection

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Smart Infrastructure and Applications

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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.

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Notes

  1. 1.

    http://openstreetmap.org

  2. 2.

    http://saplumira.com/

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Author information

Authors and Affiliations

  1. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Ebtesam Alomari & Iyad Katib

  2. High Performance Computing Center, King Abdulaziz University, Jeddah, Saudi Arabia

    Rashid Mehmood

Authors
  1. Ebtesam Alomari
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  2. Rashid Mehmood
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  3. Iyad Katib
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Correspondence to Ebtesam Alomari .

Editor information

Editors and Affiliations

  1. High Performance Computing Center, King Abdulaziz University, Jeddah, Saudi Arabia

    Rashid Mehmood

  2. Nvidia AI Technology Center, Singapore, Singapore

    Simon See

  3. Faculty of Computing and Information Technology (FCIT), King Abdulaziz University, Jeddah, Saudi Arabia

    Iyad Katib

  4. European Alliance for Innovation, Gent, Belgium

    Imrich Chlamtac

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Alomari, E., Mehmood, R., Katib, I. (2020). Sentiment Analysis of Arabic Tweets for Road Traffic Congestion and Event Detection. In: Mehmood, R., See, S., Katib, I., Chlamtac, I. (eds) Smart Infrastructure and Applications. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-13705-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-13705-2_2

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