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Extracting Major Topics From Survey Text Responses Using Natural Language Processing

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Technical Report

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Naval Postgraduate School Monterey United States

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In this thesis, we enhance the comment analysis approach of Cairolis 2017 Naval Postgraduate School thesis, to help the fleet analyze comment responses in Department of Defense surveys and utilize the results to make important decisions. This methodology automates applying descriptive labels to a comment and then uses those labels to categorize comments into a small set of meaningful prevalent topics. We apply this methodology to comments from two recent surveys a command climate survey as well as an investigation survey looking into the recent increase of physiological episodes experienced by T-45 and FA-18 aircrews. When applying novel approaches to different data, unexpected matters emerge. These matters shed light on areas of the approach that may need expansion or modification. Motivated by our analysis of text comments from two very different Navy surveys, we extend Cairolis approach in four ways. Our modifications lead to a generalized model an approach independent of the need to acquire and preprocess an external reference corpus more automation of the topic discovery process and an added element that allows a comment to have more than one topic.

Subject Categories:

  • Military Forces and Organizations
  • Test Facilities, Equipment and Methods

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