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Python in Digital Scholarship

This guide will provide an introduction to using Python in research and instruction and what resources are available in the Freedman Center.

Python in Text Analysis and Natural Language Processing (NLP)

Python is widely used for text analysis and Natural Language Processing (NLP) tasks in fields such as linguistics, digital humanities, social sciences, and computer science research. Its popularity in this area is due to powerful libraries and an intuitive syntax for handling strings and text data.

Some important tools and libraries for text analysis in Python include:

  • NLTK (Natural Language Toolkit): NLTK is a comprehensive platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources (such as WordNet), along with a suite of text processing functions.  With NLTK, one can perform tasks like tokenization (splitting text into words or sentences), stemming/lemmatization (reducing words to their root form), part-of-speech tagging, parsing syntactic trees, and even simple classification for tasks like sentiment analysis. NLTK has been a go-to library in academia for teaching NLP concepts due to its extensive documentation and ease of use.  NLTK is excellent for learning and prototyping. It is free and open source and comes with many sample datasets and exercises The NLTK Book is a recommended resource for beginners.

  • spaCy: spaCy is a modern library for advanced NLP tasks, designed with industrial-strength performance in mind. It’s optimized for large volumes of text and is written in Cython for speed. spaCy provides pre-trained models that can perform tokenization, part-of-speech tagging, named entity recognition (identifying names of people, organizations, locations, etc.), dependency parsing, and more, all in a very efficient manner. SpaCy is often used in production scenarios and research when speed and accuracy are important. It’s also open-source and easy to use, with an API that allows one to process text with just a few lines of code. For example:

    import spacy
nlp = spacy.load("en_core_web_sm")  # load a small English model
doc = nlp("Barack Obama was the 44th President of the United States.")
print([ent.text for ent in doc.ents], [ent.label_ for ent in doc.ents])
# Output: ['Barack Obama', 'the United States'] ['PERSON', 'GPE']

    This shows spaCy identifying “Barack Obama” as a person and “the United States” as a geopolitical entity. SpaCy is considered a production-focused NLP library emphasizing speed and efficiency, and it is widely used in both industry and academic research for tasks like information extraction and NLP pipeline development.

  • Other Libraries: Depending on the analysis, other libraries might be useful:

    • Gensim: for topic modeling (e.g., discovering themes in a collection of documents using algorithms like LDA).

    • Textblob: a simpler library for basic NLP tasks including sentiment analysis, built on top of NLTK.

    • Transformers (via Hugging Face): for using advanced language models (discussed in the next section on LLMs).

    • Regular Expressions (re module): for low-level pattern matching and text cleaning tasks.

In text analysis, typical workflow steps are reading in text data (from files, PDFs, web pages, etc.), preprocessing (tokenizing, removing stop words, normalizing case, etc.), analyzing the content (computing word frequencies, extracting keywords, sentiment, topics), and possibly visualizing results (e.g., word clouds or frequency plots). Python’s text handling capabilities (like easy string manipulation and powerful libraries) make it a top choice for researchers dealing with qualitative or textual data. Whether one is analyzing literature, social media posts, or transcribed interviews, Python provides the tools to efficiently turn raw text into meaningful insights.