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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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The text-package: An R-package for analyzing and visualizing human language using natural language processing and

Oscar Kjell1, Salvatore Giorgi2, H Andrew Schwartz3

  • 1Department of Psychology, Lund University.

Psychological Methods
|May 1, 2023
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Summary
This summary is machine-generated.

This tutorial introduces text, an R-package for analyzing human language using advanced Natural Language Processing (NLP) and Deep Learning (DL) transformers. It offers user-friendly tools for psychological and social science research, making complex methods accessible.

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Area of Science:

  • Psychology
  • Computational Linguistics
  • Data Science

Background:

  • Human language contains rich psychological information.
  • Advances in Natural Language Processing (NLP) and Deep Learning (DL) offer powerful tools for language analysis.
  • Existing state-of-the-art methods are not easily accessible or optimized for psychological research.

Purpose of the Study:

  • Introduce text, a new R-package for analyzing and visualizing human language using transformer models.
  • Provide accessible, user-friendly functions for psychological and social science hypothesis testing.
  • Offer both off-the-shelf solutions and a framework for advanced users.

Main Methods:

  • Utilizes transformer-based techniques from NLP and DL.
  • Provides functions for text embedding (textEmbed()), predictive modeling (textTrain(), textPredict()), and semantic similarity/distance calculation (textSimilarity(), textDistance()).
  • Includes extended methods for examining and visualizing text in embedding spaces (textProjection(), textCentrality()).

Main Results:

  • The text-package offers modular access to state-of-the-art language models.
  • It provides end-to-end solutions tailored for human-level analyses in social sciences.
  • Functions are designed with reliable defaults for ease of use with both small and large datasets.

Conclusions:

  • The text-package democratizes advanced NLP and DL for psychological researchers.
  • It facilitates hypothesis testing and data visualization in human language analysis.
  • This tool enables novel research pipelines for exploring psychological insights from text data.