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Topic Modeling

Topic Modeling is a computational method that discovers hidden thematic structure in large document collections.

Definition​

Topic modeling algorithms analyze word patterns to automatically identify "topics" — clusters of words that frequently appear together. The most common algorithm is Latent Dirichlet Allocation (LDA).

How It Works​

  1. Input: A collection of documents
  2. Process: The algorithm identifies word co-occurrence patterns
  3. Output: A list of topics (each topic is a cluster of related words)

Example Output​

Topic 1: war, battle, army, soldiers, victory
Topic 2: trade, market, price, merchants, goods
Topic 3: church, religion, god, faith, priest

Uses in Historical Research​

  • Tracking how themes change over time
  • Discovering unexpected connections between documents
  • Organizing large archives for exploration

Limitations​

  • Results require interpretation — topics are just word clusters
  • Sensitive to preprocessing choices
  • Works best with longer documents