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Mining United Nations General Assembly Debates

arXiv 2406.13553 ↗First posted 19 Jun 2024cs.CL cs.IRcited by 0

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Abstract (arXiv)

This project explores the application of Natural Language Processing (NLP) techniques to analyse United Nations General Assembly (UNGA) speeches. Using NLP allows for the efficient processing and analysis of large volumes of textual data, enabling the extraction of semantic patterns, sentiment analysis, and topic modelling. Our goal is to deliver a comprehensive dataset and a tool (interface with descriptive statistics and automatically extracted topics) from which political scientists can derive insights into international relations and have the opportunity to have a nuanced understanding of global diplomatic discourse.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 10 references that are in the map. First posted on arXiv 19 Jun 2024.

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References · 3

  1. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  2. 2019Topic Modeling in Embedding Spacescited by 224
  3. 2017Understanding State Preferences With Text As Data: Introducing the UN General Debate Corpuscited by 11

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