Developed Custom Embedder and Analyzed Political Clusters

• Day: 2025-03-07
• Time: 16:05 to 17:55
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Embedder, Text Embeddings, Political Analysis, Clustering, FAISS

Description

Session Goal

The session aimed to enhance text embedding techniques by developing a custom Embedder class and to analyze political and philosophical document clusters for strategic insights.

Key Activities

• Developed a custom Embedder class integrating OpenAI and Hugging Face models for efficient text embeddings, including batch processing and caching.
• Fixed FAISS integration issues by converting embeddings to NumPy arrays for compatibility.
• Conducted a comprehensive analysis of document clustering, focusing on similarity matrices, dendrograms, and eigenvalue decomposition.
• Explored thematic clusters within political and philosophical notes, identifying patterns in group dynamics and leadership strategies.
• Analyzed dendrogram clusters to understand political power dynamics, narrative control, and strategic positioning.

Achievements

• Successfully implemented a custom Embedder class with enhanced capabilities.
• Resolved FAISS integration challenges, ensuring smooth data handling for similarity searches.
• Identified key themes and strategic insights from political document clusters, providing a foundation for future research and publication.

Pending Tasks

• Upload and analyze the similarity matrix data for further eigenvalue analysis.
• Clarify the definition and use of groups_component in clustering analysis.

Evidence

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• event_ids: []