1. Text visualisation

2. Aims Examine some visualisations
Understand the uses of visualisation and consider the
advantages
disadvantages
Critically assess particular visualisations

3. Some considerations
Corpus analysis is becoming complex and multi-dimensional
Corpora are heavily marked-up
many dimensions of information
difficult to work with directly
difficult to see all the possible relations in a multidimensional space (text is linear; info is multidimensional)

4. (big) Data visualisation
Some corpora are billions of words
In the digital world, big data is common
Some visualisation is necessary
Use visualisation for:
presentation
query/analysis of data
exploration (and analysis)

5. How to represent data All visual elements should be meaningful
E.g.. no 3-D graphs to represent two dimensions of information
Visualisation helps to reveal complex processes

6. Networks
© Stephen Eick, Bell Labs

7. Edward Tufte
guru of representation of data

8. Simple visualisation – presentation of data

9. Graph Simple Easy to understand (axes are labelled)
No distortion of the data
Colour is meaningless – contrasting colours used to distinguish the lines on the graph

11. Birthday visualisation
Works well
We are familiar with the frame (months/days) (What is the frame for a text?)
Frequency represented by intensity of colour

13. Word cloud

14. Visualisation – (good and bad)

15. Zooming in

16. Highlighting hapax legomena

17. COCA

18. Part-of-speech only

21. Networks
at Enron -- Jeffrey Heer

24. TextArc

25. TextArc

26. Textarc

27. Voyant tools

28. Representing text Series of pages Linear block Circle - Spiral Network
Multiple-layers (and links)
Word plus connections (stats)

29. Corpora and text visualisation
How can we deal with complexity of corpora?
Uses XML structures to hide/reveal dimensions of the corpus
Browse looking for patterns ??
Zoom in on areas of interest
Switch to non-visual mode for analysis

30. Analysis metaphors Object-oriented -- transform yourself to show X
Zoom and pan -- through text

31. Issues in Visualization
Visualization is a transformation of data
How to transform in a revealing way
How to transform without giving a false picture
Basic problem -- how to represent a text

32. Text Analytics New commercial application of text analysis
Follows on from Google analytics etc.
Extracting info from unstructured documents (such as customer s, customer complaints)

33. Sentiment analysis

34. Tolkein books

35. GraphColl