This three-part workshop series introduces participants to natural language processing (NLP) with Python. It builds on our text mining series, "Getting Started with Textual Data," by extending the scope of data-inflected text analysis to include various methods of modeling meaning. Sessions will cover NLP topics ranging from segmentation and dependency parsing to sentiment analysis and context-sensitive modeling. We will also discuss how to implement such methods for tasks like classification. Basic familiarity with analyzing textual data in Python is required. We welcome students, postdocs, faculty, and staff from a variety of research domains, ranging from health informatics to the humanities.

Workshop dates were May 23, May 25, and May 27, 2022, 10:00 AM – 12:00 PM.

By the end of this series, you should be able to:

• Use popular NLP frameworks in Python, including Gensim and spaCy
• Explain key concepts and terminology in NLP, including dependency parsing, named entity recognition, and word embedding
• Process texts to glean information about sentiment, subject, and style
• Classify texts on the basis of their features
• Produce models of word meanings from a corpus
• Perform a few core NLP tasks including keyword analysis, relation extraction, document similarity analysis, and text summarization.

Software needed: Python; Google Colab (instructors will provide notebooks and data).

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