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arXiv Computation and Language By Jakob Morales, Monica Hegde, Fayeq Jeelani Syed

Analyzing Public Discourse on Urbanism: Topic Clustering, Sentiment Analysis and Retrieval-Augmented Generation using YouTube Comments

Read the original on arXiv Computation and Language →

The paper introduces a pipeline and conversational system that processes 22,788 YouTube transcript and comment chunks from 309 North American cities to analyze public discourse on urbanism. It combines geographic entity resolution, topic modeling, sentiment analysis, and Retrieval-Augmented Generation (RAG), and reports empirical findings on model performance, such as a Twitter-tuned RoBERTa classifier outperforming VADER and dense retrieval surpassing TF‑IDF. The study also evaluates groundedness metrics, noting limitations of BERTScore and ROUGE‑1 for short user-generated text.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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