Currently submitted to: JMIR Formative Research
Date Submitted: Sep 27, 2019
Open Peer Review Period: Sep 27, 2019 - Nov 10, 2019
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Development of the ASOC (Automated Semantic Occupation Coding) Algorithm
In many community-based research studies, occupational information is needed to augment existing data sets. Such information is usually solicited during interviews with open-ended questions, like “what is your job?” and “what industry sector do you work in?” Before being able to use this information for further analysis, the responses need to be categorized using a coding system , like the Canadian National Occupational Classification (NOC). This is typically carried out by manual coding, which is a time-consuming and error prone activity, suitable for automation.
To facilitate automated coding we proposed to introduce a robust algorithm that is able to identify the NOC (2016) codes using only a job title and supplemented by industry information as input. Using manually coded data sets we sought to benchmark and iteratively improve the performance of the algorithm.
We developed the ASOC (Automated Semantic Occupation Coding) algorithm, based on the National Occupational Classification (NOC) 2016, which allows users to match NOC codes with job titles and industry titles. We employed several different search strategies in the ASOC algorithm to find the best match, including: Exact Search, Minor Exact Search, Like Search, Near (same order) Search, Near (different order) Search, Any Search, Weak Match Search. In addition, Bayes rule was applied in the algorithm to choose the best matching codes.
ASOC was applied to 500 manually coded job titles and industry titles. The accuracy rate at the 4-digit NOC code level was 58.66% and improved when broader job-categories were considered (65.01% at the 3-digit NOC code level, 72.26% at the 2-digit NOC code level, 81.63% at the 1-digit NOC code level).
ASOC is a robust algorithm for automatically coding to the Canadian National Occupational Classification system, and has been evaluated using real world data. It allows researchers to codify data by occupation in a timely and cost-efficient manner, so that further analytics are possible.
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