Text and Data Mining Using Cultural Heritage Data
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The document discusses text and data mining (TDM) using cultural heritage data, highlighting its potential to enhance research collaboration and accessibility in the EU. It outlines various methodologies and tools involved in TDM, such as word clouds, network analysis, and semantic text mining, as well as their application in historical and cultural research projects. Additionally, it addresses challenges related to data access and reproducibility in TDM initiatives within Europe.
Text and Data Mining Using Cultural Heritage Data
- 1. Text and Data Mining Using Cultural Heritage Data: Opportunities and challenges Melanie Imming EU Projects manager, LIBER
- 2. LIBER: Association of EU Research Libraries
- 3. TDM Cultural Heritage OpenMinTeD Open Text and Data Mining Platform for Open Scientific Content • focuses on interoperability across mining services and content providers • So that researchers can collaboratively create, discover, share and re-use open texts and data • Improve uptake of text and data mining (TDM) in the EU • Raise awareness of TDM • Develop solutions to barriers together with stakeholders
- 4. Text and Data Mining: How big is big? Mining: •More data than you can process yourself in reasonable amount of time •Data that require computational intervention to make more sense of it all Not Macro vs Micro Making use of these techniques, data sets or new methods is not automatically choosing to ‘go big’: •Can be about one Work of Art •Not Event History vs Longue Durée TDM Cultural Heritage
- 5. What? In research projects: •Basic text mining: e.g. Word Clouds •Network analysis •Topic Modelling Mining Cultural Heritage Images © prof. dr. Joris van Eijnatten
- 6. How did newspapers in the twentieth century frame Europe? Comparitive analysis of cultural patterns in time and space prof. dr. Joris van Eijnatten Toolbox 1 Read stuff ( use your eyes) 2 Time line generator (nGram viewers) 3 Semantic tekst mining tool (texcavator) 4 Corpus linguistics (e.g. Antconc, CasualConc, Wordsmith) 5 Topic modelling (e.g. Mallet) 6 Tekst analytics suite ( SPSS Modeler) 7 Vector-space modeling (ShiCo)
- 7. An Epidemiology of Information: Data Mining the 1918 Influenza Pandemic • Harness the power of data mining techniques with interpretive analytics of the humanities and social science • integrated traditional interpretive analysis (close readings of texts) with dynamic temporal segmentation (topic modeling and segmentation) and tone analysis • Research can provide methods for understanding the spread of information and the flow of disease in other societies facing the threat of pandemics U. of Kentucky A Digging into Data project: A Trans-Atlantic Platform for the Social Sciences and Humanities, representing 11 nations from both sides of the Atlantic.
- 8. Welt der Kinder - Children and their World KNOWLEDGE OF THE WORLD AND ITS INTERPRETATION IN TEXT BOOKS AND CHILDREN’S LITERATURE, 1850-1918 Prof. Dr. Iryna Gurevych •Representations and interpretations of the world in the period from 1850 until 1918 •Over 600.000 digitalized pages “G. B. Wadström unterrichtet einen Negerprinzen” aus: Wilmsen, Friedrich Philipp: Fremde Länder und Völker, Berlin 1815, Frontispiz.
- 9. Welt der Kinder - Children and their World • Combining an established hermeneutic methodology with innovative methods and technologies • Close cooperation between historians, information scientists, and computer scientists • Developing reusable tools for the analysis of large (digital) corpora • Test model for future similar projects
- 10. Authorship attribution Mike Kestermont, assistant professor, University of Antwerp •Stylometry (computational stylistics): computational algorithms which can automatically identify the authors of anonymous texts through the quantitative analysis of individual writing styles Who wrote the lyrics of the Wilhelmus, the oldest national anthem in the world?
- 11. Authorship attribution The Wilhelmus is traditionally ascribed to Philips of Marnix, Lord of Saint-Aldegonde By using these computational stylistics, a new possible candidate came up: Peter Datheen, a second-rate sixteenth-century poet from French Flanders Datheen wasn’t on the Short List: but he came up when using a control group to validate the method
- 12. Workshop Nov 2015: “Text and Data Mining in Europe: Challenges and Action”
- 13. Elsevier TDM Policy • Access through API only • Text only- no images, tables • Research must register details • Click-through licence • Terms can change any time • Reproducibility of results