UNGA Topic Analysis
In this project we have tried to unravel the complex tapestry of international dialogue, we embarked on a comprehensive analysis of United Nations General Assembly (UNGA) speeches spanning nearly five decades. Our goal? To identify and track the evolution of key themes that have shaped global discourse from 1971 to 2018.
To tackle this monumental task, we employed two cutting-edge topic modeling techniques: Latent Dirichlet Allocation (LDA) and Topic GPT. These methods, while both aimed at uncovering latent themes in large text corpora, approach the challenge from distinctly different angles.
LDA: The Efficient Generalist
LDA, a statistical method, treats each document as a mixture of topics and each topic as a mixture of words. It's like looking at a forest from afar – you see the broad patterns but might miss the intricate details of individual trees.
Key characteristics of LDA:
- Fast and computationally efficient
- Provides a broad overview of major themes
- Relies on word frequency and co-occurrence
- Struggles with context and semantic relationships
Topic GPT: The Nuanced Specialist
Topic GPT, leveraging the power of large language models, dives deeper into the semantic essence of the text. It's akin to examining each tree in the forest, understanding not just what's there, but how everything relates.
Key characteristics of Topic GPT:
- Captures nuanced and specific themes
- Understands context and semantic relationships
- Identifies complex, multi-word concepts
- Computationally intensive and time-consuming
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Whatever it is, the way you tell your story online can make all the difference.
Our analysis revealed fascinating insights into the changing landscape of global priorities. While both methods highlighted the enduring importance of peace, development, and international cooperation, Topic GPT uncovered more specific trends like the rise of climate change discourse and the impact of the Millennium Development Goals.
One of the most striking findings was the evolution of environmental topics. While LDA picked up on general terms like "environment" and "climate," Topic GPT traced the emergence of specific concepts like "sustainable development" and "climate change adaptation."
The differences between LDA and Topic GPT became even more apparent when we clustered the speeches. LDA's clusters provided a high-level view, broadly separating global powers from smaller nations. Topic GPT, on the other hand, revealed more nuanced groupings based on specific policy focuses and regional concerns.
In conclusion, our dual-model approach offered a comprehensive view of UNGA speeches, combining LDA's efficiency in identifying broad trends with Topic GPT's ability to capture nuanced, evolving concepts. This project not only sheds light on the changing face of global dialogue but also demonstrates the complementary strengths of different NLP techniques in understanding complex, long-term textual data.
As we look to the future, this analysis provides valuable insights for policymakers and researchers alike, offering a data-driven perspective on the trajectory of international cooperation and the emerging challenges that will shape our global community in the years to come.
For a comprehensive read, please refer to the paper at [link].