Latent Dirichlet Allocation (LDA) topic modeling is a method for analyzing the distribution of semantic word clusters, so-called “topics” in collection of texts. In history it can be used for exploring the content of a corpus of digital sources.
Topic modeling is based on a statistical analysis of co-occurrence for words. It does not use dictionaries or external training data. Topic modeling is a flexible method for computational text analysis.
The Topics Explorer of DARIAH-DE is designed as a very user friendly tool. It allows researchers to experiment with topic modeling on their own computers.
The work flow from plain text to visualized results is done in a graphical user interface. The software allows users to load a collection of plain-text or xml-files and to analyze it by means of the LDA algorithm.
It runs on Windows, MacOS and Linux without installation. Try it here: https://dariah-de.github.io/TopicsExplorer/
OpenEdition suggests that you cite this post as follows:
Guido Koller (May 23, 2020). Exploring Topics in Historical Sources. We think History. Retrieved October 6, 2024 from https://doi.org/10.58079/vaf5