Use the IPython interactive interface to fetch volume content, and then run vector space model and topic modeling on volumes' OCR content. It uses the inpho/vsm python package, a textual semantics package developed by Dr. Colin Allen and his team locally at IU.
This use case obtains some HTRC volume content, builds topic models based on the content, and then visualizes the topic models in a web browser.
This use case can be run in only secure mode in the VM. To export experiment results out of the VM, you need to release the result files in secure mode, and then receive results via email.
First, switch the VM mode to secure mode (done in the HTRC portal).
In the VM, start a Terminal, and change directory to the htrc-data folder
List the files of this folder
Following are the files related to this analysis.
- htrc-demo.sh - This is the script for topic modeling analysis.
- htrc-id - This file contains the list of volume ids.
Run the topic modeling analysis
Before running the topic modeling analysis, please check the script whether the 'secure_volume' path is mentioned correctly. Correct path should be '/media/secure_volume'
You will see something like this in the console. This means the program is building topic models on the volume content.
It will take quite a while to finish the topic modeling due to the nature of this kind of computation. After the topic modeling process is done, you can view the result through the browser. (The browser will be automatically opened for you).
You will find the scripts run into errors if the VM is in maintenance mode. It is because this use case fetches HTRC content by using the Data API, which is only accessible in the secure mode.
This demo code:
- loads data from HTRC Data API
- builds an LDA topic model from the corpus
- save the LDA trained model
- view topics in a web browser in an interactive way