Digital Scholarship: Text Analysis

Text analysis is a broad term for various software tools and methods used to read, analyze, explore and manipulate text.

Text analysis plays a pivotal role in advancing academic research by enabling scholars to explore, analyze, and interpret vast amounts of textual data across diverse disciplines. By leveraging text analysis techniques and tools effectively, researchers can unlock valuable insights, facilitate knowledge discovery, and drive innovation in their respective fields.

Some common modes of text analysis are:

• Word Frequency and Lexical Diversity - Counts of words in various scenarios, for example, graphs of frequency over the course of a text
• Part of Speech Tagging - Allows investigation into the structure of language
• Topic Modelling - Groups words that appear in similar patterns which may be then characterized by a theme
• Sentiment analysis - Uses a weighted dictionary to grade the positive or negative emotions being expressed in a text
• Document Classification - Train software on a specific type of document, financial statements for example, then ask it to find similar documents in a collection.

What is Open Source?

Why is it essential for sustainable research?

How do I get help?

Learn More at the Open Source Libguide!