A Classification Model for Road Traffic Incidents on Twitter Data

被引：2

作者：

Raksachat, Thawatchai ^{[1
]}

Chawuthai, Rathachai ^{[1
]}

机构：

[1] King Mongkuts Inst Technol Ladkrabang, Sch Engn, Bangkok, Thailand

来源：

2022 37TH INTERNATIONAL TECHNICAL CONFERENCE ON CIRCUITS/SYSTEMS, COMPUTERS AND COMMUNICATIONS (ITC-CSCC 2022) | 2022年

关键词：

Deep Learning; Imbalance Dataset; Twitter Data Analytics; Road Traffic Incident; Text Classification;

D O I：

10.1109/ITC-CSCC55581.2022.9894853

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This study aims to create a classification model for road traffic incidents in Thailand using Twitter data. The challenging issue of our work is to deal with highly imbalanced dataset of 5 classes. As we surveyed, some pieces of research solved this issue by the Markov Chains method. However, using the Markov Chains in our dataset provides low performance, so we study the Undersampling, Oversampling, Markov Chains, and Bi-directional Long Short-Term Memory (Bi-LSTM). As we use the Markov Chains as the baseline, the result of our experiment found that using Bi-LSTM provides the improvement of F1-score up to 15.44% against the baseline.

引用

页码：442 / 445

页数：4

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