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 .
Second, edit the username and password with your portal username and password. The file is in ~/demo/vsm/DownloadVolumes.py. See the screenshot below. This is needed for HTRC Data API client.
(done in the HTRC portal).
In the VM, start a Terminal, and change directory to the vsm experiment 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 popped-up browser. Click on the HTRC_vsm_corpus.ipynb
In the HTRC_vsm_corpus.ipynb notebook, run all the scripts by clicking on "Cell" -> "Run All" on menu of the top of the page.
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). Click on the "Topic" button.
You will find the scripts run into errors if the VM is in maintenance mode.
The demo code in HTRC_vsm_corpus.ipynb takes one HTRC volume, and
- cleans up the content by handling page headers, line breaks, and hyphens
- Builds a Corpus object. It excludes words of which frequency < 3
- Saves the corpus object for future revisit
Then let's open another IPython notebook, HTRC_vsm_model.ipynb (list of IPython notebooks can be found at 127.0.0.1:8888/tree in the VM)
Run all the demo codes there by clicking on "Cell" -> "Run All"
This demo code:
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 3 volumes in HathiTrust using the HTRC Data API
- builds an LDA topic model from the corpus
- save the LDA trained model
- view topics
- display topics that relate to a list of words
- display documents that are most likely generated by a specific topic
- cluster topics based on LDA result
- visualize clustered topics in 2-Din a web browser in an interactive way
Here are the scripts used in this example: topicexplorer-demo.zip