When the Letters of 1916 corpus is clustered to the 16 topics generated with Gensim and Mallet it seems that 16 topics might be too much. In one of my last posts I have shown visualisations created with Gephi, and I colored the letter nodes based on the categories that was assigned by the person that uploaded the letter. Only letters assigned to four or five of these categories actually clustered together. So after I talked with my internship supervisor Dermot it was decided that I try to reduce the number of topics to see what happens, and I would create visualisations for 4, 8, 12 generated topics. I could observer that that with 4, 8, and 12 topics the clustering was still the same as with 16 topics. However, lesser topics shows that many letters from generic categories such as 1916 Rising, or The Irish Question cluster with one of the four distinct topics.
At first I generated 16 topics (the reason is explained in a previous post) with Gensim and Mallet. When I visualised my data with Gephi I got an interesting result.
Mallet – 16 topics
Gensim – 16 topics
The Mallet output shows clearly a clustering of human assigned topics (colors) around computer generated topics (the black nodes, numbered Topic 0 – 15). At least letters assigned to four topics seem to cluster also together based on computer generated topics: Letters categorised as World War 1, Family life, Official documents and Love letters. See for instance, the clustering of letters assigned to the category of WW1 and Family life. It seems that the language of letters with these two categories are quite close:
The above mentioned categories cluster quite nicely. Another observation is that the green nodes for the categories Easter Rising and Irish question are all over the place and it is questionable if this is a useful category. The remaining categories are not used much at the moment, and they are not really visible. However, they could get more important when the data set grows.
The visualisation of the Gensim topics is not so clear at first glance, because there are many more edges. But a similar red, blue and yellow clustering can be observed. One issue with the Gensim algorithm was however that it responded much more to address information in the letters, and this influences the topic modelling process. This can be observed when looking at the generated topics, the clustering of the letters and the transcriptions of the individual letters. Address information is currently part of the transcription. The plan for the future it to encode the letters in TEI. When they are TEI encoded the stripping out of address information, salutation, etc. will be easier and much clearer topics can be generated.
Gephi is a suit for interactive visualisation of network data. It is very often used for topic modelling in the Digital Humanities. As an introduction I suggest just play around with it, a how-do reading would be Gephi for the historically inclined. The best is however to get a few data sets and just try to use Gephi. For examples see the following blogs:
Essentially a challenge is to transform the output you get from Mallet or Gensim into a useful input for Gephi (edges and nodes files). On his blog Elijah goes into detail explaining how he visualized the Mallet output.
I wrote a function in my export/outputter module that converts Mallet output to Gephi edges data and saves it to a file. To view the module feel free to have a look at my project on GitHub.