Series overview:

Text-mining refers to techniques which can involve the collection, processing and parsing of text derived from a range of sources (e.g., corpora, digital libraries, web forums). Text-mining is often performed with the goal of analyzing text to gain insights not readily available without the use of digital methods. It is useful to or shares methods with fields which seek to understand words and their context using computer friendly representations (e.g., word embeddings, or word vectors) such as natural language processing, computational models of language, psycholinguistcs, or digital humanities. In this workshop series, students and researchers will learn text-mining and natural language processing (NLP) techniques using the Python programming language for a range of use-cases. Four workshop sessions are currently available. Participants may choose to attend whichever of these self-contained sessions they find useful, but it is recommended that participants who are new to this topic attend at least Session 2. Readers who are interested in learning about additional tools and methods for text-mining and computer-based analysis of natural language are encouraged to consult the recently published text-mining guide, written by the text-mining support group at Information & Library Services (textminingsupport@ru.nl).. Questions about these workshops or their contents can be sent via email to daniel.sharoh@ru.nl.

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Session 2: Introduction to the analysis of natural language in Python

Do you want to learn how to use Python to analyze natural language? The Digital Competence Centre organizes a workshop session on this topic which is suitable for participants with a broad range of skill-levels.

This workshop provides an introduction to preprocessing, manipulating and analyzing linguistic and textual properties of natural language text in Python, with limited time dedicated to basic concepts in the Python language. Participants in this workshop will learn how to use the Natural Language Toolkit (NLTK) to segment or tokenize a given text in English. They will also learn techniques to analyze text based on this information. This could mean, for example, identifying the individual sentences and paragraphs in a text, labeling all words in a text with a part-of-speech tag, and analyzing word-frequency or co-occurrence statistics for all identified nouns. As a case-study, participants will perform a sentiment analysis (similar to the concept of valence) to quantify properties of the text that can be related to aspects of its emotional content. After this workshop, participants will be prepared to further develop their programming skills individually, and they will be able to write and automate simple text processing steps that can be integrated into a larger workflow.