PsychWordVec

Word Embedding Research Framework for Psychological Science.

An integrated toolbox of word embedding research that provides:

1. A collection of pre-trained static word vectors in the .RData compressed format;
2. A series of functions to process, analyze, and visualize word vectors;
3. A range of tests to examine conceptual associations, including the Word Embedding Association Test (Caliskan et al., 2017) and the Relative Norm Distance (Garg et al., 2018), with permutation test of significance;
4. A set of training methods to locally train (static) word vectors from text corpora, including Word2Vec (Mikolov et al., 2013), GloVe (Pennington et al., 2014), and FastText (Bojanowski et al., 2017);
5. A group of functions to download pre-trained language models (e.g., GPT, BERT), extract contextualized (dynamic) word vectors (based on the R package text), and perform language analysis tasks (e.g., fill in the blank masks).

⚠️ All users should update the package to version ≥ 0.3.0. Old versions (≤ 0.2.0) may run slowly, and some old functions have been deprecated.

Author

Han-Wu-Shuang (Bruce) Bao 包寒吴霜

Email: baohws@foxmail.com

Homepage: psychbruce.github.io

Citation

• Bao, H.-W.-S. (2022). PsychWordVec: Word embedding research framework for psychological science. R package version 0.2.x. https://CRAN.R-project.org/package=PsychWordVec

Installation

## Method 1: Install from CRAN
install.packages("PsychWordVec")

## Method 2: Install from GitHub
install.packages("devtools")
devtools::install_github("psychbruce/PsychWordVec", force=TRUE)

Types of Data for PsychWordVec

Functions in PsychWordVec

• Word Embeddings Data Management and Transformation
  • as_embed(): from wordvec (data.table) to embed (matrix)
  • as_wordvec(): from embed (matrix) to wordvec (data.table)
  • load_embed(): load word embeddings data as embed (matrix)
  • load_wordvec(): load word embeddings data as wordvec (data.table)
  • data_transform(): transform plain text word vectors to wordvec or embed
• Word Vectors Extraction, Linear Operation, and Visualization
  • subset(): extract a subset of wordvec and embed
  • normalize(): normalize all word vectors to the unit length 1
  • get_wordvec()
  • sum_wordvec()
  • plot_wordvec()
  • plot_wordvec_tSNE(): 2D or 3D visualization with t-SNE
  • orth_procrustes(): Orthogonal Procrustes matrix alignment
• Word Semantic Similarity Analysis, Network Analysis, and Association Test
  • cosine_similarity(): cos_sim() or cos_dist()
  • pair_similarity()
  • plot_similarity()
  • tab_similarity()
  • most_similar(): find the Top-N most similar words
  • plot_network(): visualize a (partial correlation) network graph of words
  • test_WEAT(): WEAT and SC-WEAT with permutation test of significance
  • test_RND(): RND with permutation test of significance
• Dictionary Automatic Expansion and Reliability Analysis
  • dict_expand(): expand a dictionary from the most similar words
  • dict_reliability(): reliability analysis and PCA of a dictionary
• Local Training of Static Word Embeddings (Word2Vec, GloVe, and FastText)
  • tokenize()
  • train_wordvec()
• Pre-trained Language Models (PLM) and Contextualized Word Embeddings
  • text_init(): set up a Python environment for PLM
  • text_model_download(): download PLMs from HuggingFace to local “.cache” folder
  • text_model_remove(): remove PLMs from local “.cache” folder
  • text_to_vec(): extract contextualized token and text embeddings
  • text_unmask(): fill in the blank mask(s) in a query

See the documentation (help pages) for their usage and details.