Text Mining

A set of computational techniques—drawing on natural language processing, machine learning, and statistical methods—for extracting structured information, patterns, and representations (e.g., topics, sentiment, entities, embeddings, classifications) from large corpora of political texts.

Computational methods that transform large collections of unstructured textual material into structured, analysable representations by combining linguistic preprocessing (tokenization, normalization), statistical feature extraction, and pattern discovery or machine-learning techniques to identify topics, entities, relations, sentiments, and structural patterns for downstream analysis.

A set of computational techniques for extracting patterns, structured information, trends, or summaries from large unstructured text collections, combining preprocessing (tokenization, normalization), linguistic analysis (parsing, named‑entity recognition), and statistical or machine‑learning methods.