Twitter-based traffic information system based on vector representations for words

TL;DR

This paper introduces a simple, robust Twitter traffic classification framework using word embeddings to distinguish traffic-related tweets with high accuracy, addressing issues of sparsity and high dimensionality in traditional methods.

Contribution

The paper proposes a novel, minimal-parameter model leveraging word embeddings for traffic tweet classification, outperforming traditional bag-of-words approaches.

Findings

Achieved 95.9% classification accuracy

Demonstrated robustness against data sparsity

Simplified model with only one trainable parameter

Abstract

Recently, researchers have shown an increased interest in harnessing Twitter data for dynamic monitoring of traffic conditions. Bag-of-words representation is a common method in literature for tweet modeling and retrieving traffic information, yet it suffers from the curse of dimensionality and sparsity. To address these issues, our specific objective is to propose a simple and robust framework on the top of word embedding for distinguishing traffic-related tweets against non-traffic-related ones. In our proposed model, a tweet is classified as traffic-related if semantic similarity between its words and a small set of traffic keywords exceeds a threshold value. Semantic similarity between words is captured by means of word-embedding models, which is an unsupervised learning tool. The proposed model is as simple as having only one trainable parameter. The model takes advantage of outstanding merits, which are demonstrated through several evaluation steps. The state-of-the-art test accuracy for our proposed model is 95.9%.