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Research Guides

Text and Data Mining Guide: Module 5: Tools & Implementation

Step-by-step guide on how to get started with your text mining project along with examples of past text mining projects from UW researchers and students.

Module 5

Module 5: Tools & Implementation

In this final module, we'll explore various tools and platforms you can use to conduct text mining, along with considerations for managing your workflow and collaborating with others.

  1. Software Environments & Key Libraries

    • Python:
      • pandas (data handling)
      • NLTK, spaCy (NLP tasks)
      • gensim (topic modeling)
      • scikit-learn (machine learning)
    • R:
      • tidytext, tm, quanteda (text manipulation and analysis)
      • caret (machine learning)
    • Both Python & R offer a wide ecosystem of libraries and community support.

  2. GUI-Based Tools & Platforms

    • RapidMiner: drag-and-drop interface for data science workflows
    • Orange: visual programming software for exploratory data analysis

  3. Command Line Tools

    • grep, awk, sed: quick text manipulation for filtering and formatting

  4. Workflow Management & Version Control

    • Git/GitHub: track changes to scripts/data and collaborate with others

Short Exercise

  • Choose one tool or platform (Python, R, RapidMiner, etc.) and outline how you would:
    • Collect text data (identify source and method)
    • Clean and preprocess (stopwords, tokenization, etc.)
    • Perform at least one analysis (word frequency, sentiment, topic modeling)

With these tools and best practices, you'll be well-equipped to tackle text mining projects. Good luck on your journey!

Fill out this Google Colab Notebook to practice the techniques in this module.