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.
Working with the 1916 data I found (what people with experience have always told me) that cleaning of your data is an essential step. It could be even the most important step. Inconsistent, messy, and fault leads to problems and wrong results in the analysis and interpretation stages of your research.
In regards to the 1916 letters wrong spelling, inconsistent markup and comments in the text, inconsistent metadata are all sources for error. I knew from the start of my internship that cleaning the 1916 data would be one of the challenges. I did a bit of research and found very useful tips. Emma Clarke a former Mphil student here in TCD did recently a topic modelling project and talking to her and reading her Mphil thesis was very helpful. Furthermore,I found the O’Reilly Bad Data Handbook an interesting read.
GoogleRefine or OpenRefine is a software that allows to explore data and clean up messy data. The data can be loaded from CSV explored and cleaned up by correcting spelling and regularising entries.
Thanks to Frank Lynam form TCD for pointing me to this useful software!