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Digital Scholarship & Digital Humanities

Description

Text Mining & Analysis involves extracting valuable insights from large volumes of text using computational techniques. It helps to identify trends, summarize content, and understand linguistic patterns, making it a powerful tool across fields such as social media research, literary studies, and data journalism. This guide introduces key tools, data sources, and sample projects to get you started with Text Mining & Analysis.

Tools

  • ANTConc: A software application for corpus linguistics that allows you to study word frequency, collocations, and concordances.
  • Gale Digital Scholar Lab: A single research platform where you can apply natural language processing tools to raw text data (OCR) from your institution's Gale Primary Sources holdings, or from uploaded OCR. 
  • Google Colab: A free, cloud-based environment for Python programming that comes with built-in support for text mining and machine learning libraries.
  • NLTK (Natural Language Toolkit): A Python library used for text processing tasks such as tokenization, stemming, and sentiment analysis.
  • R: A powerful statistical language that offers text analysis packages like tm and quanteda for data visualization and mining.
  • Readex TextExplorer: A resource for analyzing large text collections, integrated with Voyant for enhanced visualization and exploration of digitized historical documents and newspapers.
  • SpaCy: A fast and efficient natural language processing library for advanced text analysis, perfect for building scalable solutions.
  • Voyant Tools: An easy-to-use, web-based platform for visualizing and analyzing text without the need for programming skills.

There are many tailored packages for specific text analysis tasks beyond the well-known libraries like NLTK and SpaCy. For example, CleanNLP provides streamlined text preprocessing capabilities, while libraries like Gensim are specialized for topic modeling and word vectorization. Depending on your project needs, you may discover additional specialized packages that can enhance your text mining workflow. 

Data Sources

  • Google Books Ngram Viewer Dataset: A rich dataset of word frequency data from millions of digitized books, useful for studying language and cultural trends over time.
  • HathiTrust Digital Library: A comprehensive collection of digitized books and journals, ideal for large-scale text analysis.
  • Kaggle: A platform with a wide range of datasets, including text-based data. While not specifically designed for text mining, it provides diverse sources that can be leveraged for various projects, such as sentiment analysis and natural language processing tasks.
  • Ole Miss Library Databases: Our library provides access to datasets, particularly for use with Readex TextExplorer, enabling detailed analysis and visualization of digitized historical documents and newspapers.
  • Project Gutenberg: A vast library of public domain books that can be freely downloaded and analyzed for text mining projects.

Sample Projects