Summary of Transit Pulse: Utilizing Social Media As a Source For Customer Feedback and Information Extraction with Large Language Model, by Jiahao Wang and Amer Shalaby
Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model
by Jiahao Wang, Amer Shalaby
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Information Retrieval (cs.IR); Machine Learning (cs.LG); 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 proposes a novel approach for analyzing social media posts about public transportation systems. It aims to extract valuable insights on service quality and identify emerging issues. The current methods, including TF-IDF and traditional sentiment analysis, are limited in their ability to capture the nuanced interactions between topics and sentiments. To address this challenge, the authors employ Large Language Models (LLMs), specifically Llama 3, with Retrieval-Augmented Generation (RAG) for domain-specific knowledge integration. The proposed method demonstrates better performance compared to traditional NLP approaches on real-world user tweet data. This breakthrough has the potential to transform social media analysis in the public transit domain, providing actionable insights and enhancing responsiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using social media to help make public transportation systems better. People share their experiences and thoughts about buses and trains online, which can be very helpful for transportation agencies. The problem is that there are too many messages to read through by hand, so the authors came up with a new way to analyze them using special computer models called Large Language Models (LLMs). These models can understand people’s language and emotions better than other methods. By combining these models with some extra information from the internet, they were able to identify important insights that can help transportation agencies respond quickly to problems. |
Keywords
» Artificial intelligence » Llama » Nlp » Rag » Retrieval augmented generation » Tf idf
