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This study introduces a new R pipeline for semantic network analysis, simplifying cognitive process research. The tools streamline data preprocessing and network analysis, making complex methods more accessible to researchers.

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Area of Science:

  • Cognitive Psychology
  • Computational Linguistics
  • Network Science

Background:

  • Semantic network analysis is valuable for studying cognitive processes but faces limited application.
  • Barriers include a lack of resources for new researchers and laborious data preprocessing.
  • Existing methods require significant expertise and time investment.

Purpose of the Study:

  • To reduce barriers to applying semantic network analysis in psychological research.
  • To provide a comprehensive pipeline for preprocessing, estimating, and analyzing semantic networks.
  • To offer an R tutorial with associated packages for efficient and reproducible analysis.

Main Methods:

  • Development of a semantic network analysis pipeline using R packages: SemNetDictionaries, SemNetCleaner, and SemNeT.
  • SemNetDictionaries and SemNetCleaner facilitate efficient, reproducible, and transparent linguistic data preprocessing.
  • SemNeT offers a graphical user interface for estimating and comparing semantic networks.

Main Results:

  • A complete start-to-finish pipeline from raw data to semantic network analysis results is presented.
  • The R packages and tutorial demonstrate a streamlined approach to semantic network analysis.
  • The pipeline effectively addresses the challenges of data preprocessing and network estimation.

Conclusions:

  • The developed pipeline and R tutorial significantly lower the barriers for conducting semantic network analysis.
  • This resource empowers both novice and experienced researchers to utilize semantic network methodologies.
  • The approach promotes efficiency, reproducibility, and transparency in cognitive process research.