A Classification Model for Road Traffic Incidents on Twitter Data

Abstract

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.