A Deep Learning Based Sentiment Analytic Model for the Prediction of Traffic Accidents
作者机构：University Institute of Information TechnologyPMAS Arid Agriculture UniversityRawalpindi46000Pakistan Department of MarketingOperationsand Information SystemAbu Dhabi UniversityAbu Dhabi6844United Arab Emirates College of Computer and Information SciencesPrince Sultan UniversityRiyadh11442Saudi Arabia Robotics and Internet of Things LabPrince Sultan UniversityRiyadh11442Saudi Arabia
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2023年第77卷第11期
页 面：1599-1615页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0837[工学-安全科学与工程] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：Prince Sultan University PSU
主 题：Accident XLNet Bi-LSTM association rule mining Twitter
摘 要：The severity of traffic accidents is a serious global concern,particularly in developing *** the main causes and contributing circumstances may reduce the severity of traffic *** 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 *** such approaches are popular,there exists an issue of data management and low prediction *** 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 ***,a Tweet dataset has been formed by using an exhaustive keyword-based searching *** 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 *** output of this phase has been forwarded to a three-layer deep learning model for final *** experiment has been performed to test the efficiency of the proposed XLNet-Bi-LSTM *** has been shown that the proposed model achieved 94.2%prediction accuracy.
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