Survey Methodology
Semi-automated classification for multi-label open-ended questions

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by Hyukjun Gweon, Matthias Schonlau and Marika WenemarkNote 1

  • Release date: December 15, 2020
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Abstract

In surveys, text answers from open-ended questions are important because they allow respondents to provide more information without constraints. When classifying open-ended questions automatically using supervised learning, often the accuracy is not high enough. Alternatively, a semi-automated classification strategy can be considered: answers in the easy-to-classify group are classified automatically, answers in the hard-to-classify group are classified manually. This paper presents a semi-automated classification method for multi-label open-ended questions where text answers may be associated with multiple classes simultaneously. The proposed method effectively combines multiple probabilistic classifier chains while avoiding prohibitive computational costs. The performance evaluation on three different data sets demonstrates the effectiveness of the proposed method.

Key Words:   Semi-automated classification; Open-ended questions; Multi-label data.

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How to cite

Gweon, H., Schonlau, M. and Wenemark, M. (2020). Semi-automated classification for multi-label open-ended questions. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 46, No. 2. Paper available at https://www150.statcan.gc.ca/n1/pub/12-001-x/2020002/article/00005-eng.htm.

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ISSN : 1492-0921

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Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

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Published by authority of the Minister responsible for Statistics Canada.

© Her Majesty the Queen in Right of Canada as represented by the Minister of Industry, 2020

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

Frequency: Semi-annual

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Semi-automated classification for text data
Section 3. Multi-label classification
Section 4. The majority-voted-based ensemble of PCC for semi-automated classification
Section 5. Experiments
Section 6. Discussion
References

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