  Vehicle hijacking is one of the leading crimes in many cities. For instance,
in South Africa, drivers must constantly remain vigilant on the road in order
to ensure that they do not become hijacking victims. This work is aimed at
developing a map depicting hijacking spots in a city by using Twitter data.
Tweets, which include the keyword "hijacking", are obtained in a designated
city of Cape Town, in this work. In order to extract relevant tweets, these
tweets are analyzed by using the following machine learning techniques: 1) a
Multi-layer Feed-forward Neural Network (MLFNN); 2) Convolutional Neural
Network; and Bidirectional Encoder Representations from Transformers (BERT).
Through training and testing, CNN achieved an accuracy of 99.66%, while MLFNN
and BERT achieve accuracies of 98.99% and 73.99% respectively. In terms of
Recall, Precision and F1-score, CNN also achieved the best results. Therefore,
CNN was used for the identification of relevant tweets. The relevant reports
that it generates are visually presented on a points map of the City of Cape
Town. This work used a small dataset of 426 tweets. In future, the use of
evolutionary computation will be explored for purposes of optimizing the deep
learning models. A mobile application is under development to make this
information usable by the general public.

Computation and Language;Machine Learning