Text Classification of installation Support Contract Topic Models for Category Management
Abstract
Category management is being implemented Air Force-wide, requiring appropriate categorization of Air Force contracts into newly created, manageable spend categories. It has been recognized that current composite categories can be further distinguished into sub-categories leveraging text analytics on the contract descriptions. Upon establishing newly constructed categories, future contracts must be classified into these newly constructed categories in order to be strategically managed. This research proposes a methodological framework for using Latent Dirichlet Allocation to sculpt categories from the natural distribution of contract topics, and assesses the appropriateness of supervised learning classification algorithms such as Support Vector Machines, Random Forests, and Weighted K-Nearest Neighbors models to classify future unseen contracts. The results suggest a significant improvement in modeled spend categories over the existing categories, facilitating more accurate classification of unseen contracts into their respective sub-categories.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 23, 2018
- Accession Number
- AD1056419
Entities
People
- William C. Sevier
Organizations
- Air Force Institute of Technology