A weakly supervised and deep learning method for an additive topic analysis of large corpora

Research output: Contribution to journalArticlepeer-review

Abstract

The collaborative effort of theory-driven content analysis can benefit significantly from the use of topic analysis methods, which allow researchers to add more categories while developing or testing a theory. This additive approach enables the reuse of previous efforts of analysis or even the merging of separate research projects, thereby making these methods more accessible and increasing the discipline’s ability to create and share content analysis capabilities. This paper proposes a weakly supervised topic analysis method that uses both a low-cost unsupervised method to compile a training set and supervised deep learning as an additive and accurate text classification method. We test the validity of the method, specifically its additivity, by comparing the results of the method after adding 200 categories to an initial number of 450. We show that the suggested method provides a foundation for a low-cost solution for large-scale topic analysis.

Original languageAmerican English
Pages (from-to)29-59
Number of pages31
JournalComputational Communication Research
Volume3
Issue number1
DOIs
StatePublished - 2021

Keywords

  • Computational content analysis
  • Deep learning
  • Natural language processing
  • Topic analysis
  • Weak supervision

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Linguistics and Language

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