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  • Use Case: Perform Text Analytics Using Topic Explorer

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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/ See the screenshot below. This is needed for HTRC Data API client. 

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In the VM, start a Terminal, and change directory to the vsm experiment htrc-data folder

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cd ~/demo/vsmhtrc-data

List the files of this folder

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Start an IPython notebook server Run the topic modeling analysis

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You will see something like this in the popped-up browser. Click on the HTRC_vsm_corpus.ipynb 

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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.

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After the topic modeling process is done, you can view the result through the browser. (The browser will be automatically opened for you).

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You will find the scripts run into errors if the VM is in maintenance modeThe 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 in the VM)

Run all the demo codes there by clicking on "Cell" -> "Run All"

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This demo code:

  • reads in a saved Corpus objectloads data from HTRC Data API
  • builds an LDA model from the corpus
  • save the LDA trained model
  • view topicsdisplay 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-D