Topic modelling is a technique by which documents within a corpus are clustered based on how certain groups of terms are used together within the text. The commonalities between such term groupings tend to form what we would normally call “topics”, providing a way to automatically categorise documents by their structural content, rather than a more metadata-based knowledge system. Using resources held with EHRI's collections, this notebook offers learners an introduction to 'LDA' topic modelling using Python in a step-by-step guide.
The European Holocaust Research Infrastructure (EHRI) has a mission to enable transnational research, commemoration and education in Holocaust studies, and to accommodate for the wide dispersal of sources and expertise across many institutions.
- The need to reference webpages in academic work is growing all the time, particularly in the digital humanities. There are many different reference management systems that exist to help researchers sort and find their sources and the most accessible of these is Zotero.
- This blog examines TEITOK, which is a corpus framework used as an alternative to Omeka. TEITOK is centered around texts and is similar to the Omeka interface – both allow you to search through the documents, and display the transcription. The main difference is that Omeka treats the transcription as an object description, whereas TEITOK not only shows that a word appears in a document, but also where it appears and how it is used.