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IJSTR >> Volume 10 - Issue 10, October 2021 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A Near Real-Time Traffic Congestion Monitoring System Using Sentiment Analysis On Twitter Data

[Full Text]

 

AUTHOR(S)

Goboitshepo Ororiseng Leroke, Manoj Lall

 

KEYWORDS

Social Media; Twitter; Traffic Congestion; Sentiment Analysis; Natural Language Processing; Machine Learning; Topic Modelling.

 

ABSTRACT

Traffic congestion is a major challenge facing urban areas around the globe today. A common approach adopted by government agencies for the monitoring of traffic conditions is by making use of CCTV cameras or electronic sensors. These approaches requires the maintenance of a large network of sensors and cameras to monitor every street in the city. This is impractical and very costly. However, with the advancement of social media in all its forms, including blogs, online forums, Facebook, and Twitter, it is possible to treat social media as a human sensor network. In this article, an alternative traffic monitoring approach that is inexpensive and provides traffic information in near real-time is developed. The proposed approach makes use of Twitter data analytics to report on the prevailing traffic conditions in a particular locality. In addition, the reason behind the traffic congestion is also highlighted. Knowing the cause of the traffic congestion is important as it gives an indication of the severity of the problem. For the modelling of the proposed near real time Twitter-based model, 5 000 tweets collected over a period of six months were collected for a particular geographical location. The relevant Twitters were pre-processing to obtain the applicable features such as the location of the origin of a particular post, the time when the tweet was posted. Random Forest, Naïve Bayes, Support Vector Machine and K-Nearest Neighbour were used in the construction of the classification model. The best performing model (Naïve Bayes) was selected for real-time tweet classification. Python’s Natural Language Toolkit (NLTK) and associated libraries, was applied to enhance the suitability of tweets for conducting sentiment analysis and topic modelling. The emotions expressed in the tweets were captured by sentiment analysis and the reason behind traffic congestions were determined by topic modelling. The location, the sentiment and the reasons for the traffic congestions were visualized using street map. It is envisaged that such a model will assist commuters in making an informed decision on route selection.

 

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