From Twitter to Reasoner: Understand Mobility Travel Modes and Sentiment Using Large Language Models

TL;DR

This paper presents a framework using Large Language Models to infer travel modes and sentiments from social media data, enabling transportation insights without manual annotation, revealing predominantly negative sentiments and their causes.

Contribution

The study introduces a novel LLM-based approach for extracting travel modes and sentiments from unstructured social media data without manual labeling.

Findings

Most social media posts show negative sentiment.

Identified factors contributing to negative sentiments.

Proposed recommendations for operators and policymakers.

Abstract

Social media has become an important platform for people to express their opinions towards transportation services and infrastructure, which holds the potential for researchers to gain a deeper understanding of individuals' travel choices, for transportation operators to improve service quality, and for policymakers to regulate mobility services. A significant challenge, however, lies in the unstructured nature of social media data. In other words, textual data like social media is not labeled, and large-scale manual annotations are cost-prohibitive. In this study, we introduce a novel methodological framework utilizing Large Language Models (LLMs) to infer the mentioned travel modes from social media posts, and reason people's attitudes toward the associated travel mode, without the need for manual annotation. We compare different LLMs along with various prompting engineering methods in light of human assessment and LLM verification. We find that most social media posts manifest negative rather than positive sentiments. We thus identify the contributing factors to these negative posts and, accordingly, propose recommendations to traffic operators and policymakers.