Summary of From Twitter to Reasoner: Understand Mobility Travel Modes and Sentiment Using Large Language Models, by Kangrui Ruan et al.
From Twitter to Reasoner: Understand Mobility Travel Modes and Sentiment Using Large Language Models
by Kangrui Ruan, Xinyang Wang, Xuan Di
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel methodological framework using Large Language Models (LLMs) to analyze social media data about transportation services and infrastructure. The authors aim to infer mentioned travel modes from unstructured social media posts and reason people’s attitudes toward those modes without manual annotation. They compare different LLMs with various prompting engineering methods, verifying their results through human assessment. The study finds that most social media posts express negative sentiments towards transportation services, identifying contributing factors and proposing recommendations for traffic operators and policymakers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers used special computer models called Large Language Models (LLMs) to understand what people think about transportation like buses or trains on social media. They wanted to find out what people liked or disliked without having someone read every post. The study looked at how different LLMs worked together with other techniques and checked their results with human helpers. Surprisingly, most people were unhappy with transportation services! The authors suggest ways for those in charge of transportation and government officials to make things better. |
Keywords
» Artificial intelligence » Prompting
