Sociological Methods & Research· 2018Q1
The Future of Coding: A Comparison of Hand-Coding and Three Types of Computer-Assisted Text Analysis Methods
- 235citations
- Q1SCImago
- 2018year
Short summary
Supervised machine learning (SML) best replicates hand-coded results for analyzing complex theoretical concepts in text, though dictionary and unsupervised methods also offer complementary strengths.
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Key points
- Supervised Machine Learning (SML) most effectively replicates hand-coded results for analyzing complex theoretical concepts in textual data.
- Dictionary and unsupervised machine learning methods also demonstrate utility, offering complementary strengths to SML and hand-coding.
- The analysis involved 1,253 news articles focusing on the concept of inequality.
- Social scientists should utilize a combination of hand-coding and various computer-assisted methods, chosen based on the specific research question.
AI-generated from the title and abstract; the full text is not read.
Abstract
Advances in computer science and computational linguistics have yielded new, and faster, computational approaches to structuring and analyzing textual data. These approaches perform well on tasks like information extraction, but their ability to identify complex, socially constructed, and unsettled theoretical concepts—a central goal of sociological content analysis—has not been tested. To fill this gap, we compare the results produced by three common computer-assisted approaches—dictionary, supervised machine learning (SML), and unsupervised machine learning—to those produced through a rigorous hand-coding analysis of inequality in the news ( N = 1,253 articles). Although we find that SML methods perform best in replicating hand-coded results, we document and clarify the strengths and weaknesses of each approach, including how they can complement one another. We argue that content analysts in the social sciences would do well to keep all these approaches in their toolkit, deploying them purposefully according to the task at hand.
The authors' abstract, as published at the source. Sociological Methods & Research, 2018 · DOI ↗
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Field: General Social Sciences
General Social SciencesSocial Sciences