Uncovering censorship in the 16th century with Transkribus and Python. Episode V: How did the censors actually change the text?

by Markus Müller

We have come a long way since episode I of this miniseries: After digitizing the texts, normalizing the orthographic variants, and resolving the abbreviations, we used an interactive web app to find and correct remaining transcription errors. Now that the texts are free of mistakes we can finally use them for comparisons. In this episode, we will compare an original text with an expurged reprint to find censorship. Since the censors sometimes manipulated only one or two characters in a word, thereby changing the meaning of the whole sentence, we will compare the texts word by word using the Python module difflib.
„Uncovering censorship in the 16th century with Transkribus and Python. Episode V: How did the censors actually change the text?“ weiterlesen

Doing Digital History with Python III: topic modelling with Gensim, spaCy, NTLK and SciKit learn

by Monika Barget

In April 2020, we started a series of case studies to introduce researchers working with historical sources to data analysis and data visualisation with Python. Today’s blog post covers topic modelling with the Python packages Gensim, spaCy, NLTK and SciKit learn.

Topic modelling is one of the central methods of Natural Language Processing (NLP), the “automatic manipulation of natural language, like speech and text, by software.” (Jason Brownlee: What Is Natural Language Processing?, in: Deep Learning for Natural Language Processing, 22nd September 2017) In its most basic form, a “topic” modelled by software displays word co-occurrences in texts, assuming that the frequency of co-occurrences defines certain areas of meaning. „Doing Digital History with Python III: topic modelling with Gensim, spaCy, NTLK and SciKit learn“ weiterlesen