@Article{cmc.2023.040455,
AUTHOR = {Nadeem Malik, Saud Altaf, Muhammad Usman Tariq, Ashir Ahmed, Muhammad Babar},
TITLE = {A Deep Learning Based Sentiment Analytic Model for the Prediction of Traffic Accidents},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {77},
YEAR = {2023},
NUMBER = {2},
PAGES = {1599--1615},
URL = {http://www.techscience.com/cmc/v77n2/54778},
ISSN = {1546-2226},
ABSTRACT = {The severity of traffic accidents is a serious global concern, particularly in developing nations. Knowing the main
causes and contributing circumstances may reduce the severity of traffic accidents. There exist many machine
learning models and decision support systems to predict road accidents by using datasets from different social
media forums such as Twitter, blogs and Facebook. Although such approaches are popular, there exists an issue
of data management and low prediction accuracy. This article presented a deep learning-based sentiment analytic
model known as Extra-large Network Bi-directional long short term memory (XLNet-Bi-LSTM) to predict traffic
collisions based on data collected from social media. Initially, a Tweet dataset has been formed by using an
exhaustive keyword-based searching strategy. In the next phase, two different types of features named as individual
tokens and pair tokens have been obtained by using POS tagging and association rule mining. The output of this
phase has been forwarded to a three-layer deep learning model for final prediction. Numerous experiment has
been performed to test the efficiency of the proposed XLNet-Bi-LSTM model. It has been shown that the proposed
model achieved 94.2% prediction accuracy.},
DOI = {10.32604/cmc.2023.040455}
}