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Text Analysis at Penn Libraries

A guide to text mining tools and methods

What Is Text Analysis?

Computational Text Analysis, Computer-aided Text Analysis, Text Mining, and the abbreviation TDM are broad terms for searching, organizing, and analyzing large amounts of text data.

Why use TDM techniques?

A Venn diagram of the intersection of text mining and six related fields (shown as ovals), such as data mining, statistics, and computational linguistics. The seven text mining practice areas exist at the major intersections of text mining with its six related fields.TDM can help reveal new patterns or information from a large body of work - leading to the development of new knowledge, of a larger evidence-based practice. TDM enables researchers to analyze thousands of documents and terabytes of data, allowing for a comprehensive look into research questions.

Examples where Researchers used text analysis to answer their research question

When to use TDM?

The methods used to process corpora vary widely between disciplines, and are based on insights from machine learning, statistics, computational linguistics, sociology, and many other fields. 

Researchers use text analysis tasks such as:

  • Sentiment Analysis
  • Topic Modeling
  • Entity Extraction
  • Part-of-speech Tagging
  • Semantic Reasoning
  • Text Classification
  • Text Summarization

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