README

PsychWordVec

Word Embedding Research Framework for Psychological Science.

An integrative toolbox of word embedding research that provides:

A collection of pre-trained static word vectors in the .RData compressed format;

A series of functions to process, analyze, and visualize word vectors;

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;

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);

A group of functions to download pre-trained language models (e.g., GPT, BERT) and extract contextualized (dynamic) word vectors (based on the R package text).

⚠️ All users should update the package to version ≥ 0.3.2. Old versions may have slow processing speed and other problems.

Citation

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

Note: This is the original citation format. Please refer to the information when you library(PsychWordVec) for the APA-7 format of your installed version.

Bao, H.-W.-S., Wang, Z.-X., Cheng, X., Su, Z., Yang, Y., Zhang, G.-Y., Wang, B., & Cai, H. (2023). Using word embeddings to investigate human psychology: Methods and applications. Advances in Psychological Science, 31(6), 887–904.
[包寒吴霜, 王梓西, 程曦, 苏展, 杨盈, 张光耀, 王博, 蔡华俭. (2023). 基于词嵌入技术的心理学研究：方法及应用. 心理科学进展, 31(6), 887–904.]

## 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

Note: Word embedding refers to a natural language processing technique that embeds word semantics into a low-dimensional **embedding matrix**, with each word (actually token) quantified as a **numeric vector** representing its (uninterpretable) semantic features. Users are suggested to import word vectors data as the `embed` class using the function `load_embed()`, which would automatically normalize all word vectors to the unit length 1 (see the `normalize()` function) and accelerate the running of most functions in `PsychWordVec`.
	`embed`	`wordvec`
Basic class	matrix	data.table
Row size	vocabulary size	vocabulary size
Column size	dimension size	2 (variables: `word`, `vec`)
Advantage	faster (with matrix operation)	easier to inspect and manage
Function to get	`as_embed()`	`as_wordvec()`
Function to load	`load_embed()`	`load_wordvec()`

Note: Word embedding refers to a natural language processing technique that embeds word semantics into a low-dimensional embedding matrix, with each word (actually token) quantified as a numeric vector representing its (uninterpretable) semantic features. Users are suggested to import word vectors data as the embed class using the function load_embed(), which would automatically normalize all word vectors to the unit length 1 (see the normalize() function) and accelerate the running of most functions in PsychWordVec.

Basic class

matrix

data.table

Row size

vocabulary size

Column size

dimension size

2 (variables: word, vec)

Advantage

faster (with matrix operation)

easier to inspect and manage

Function to get

as_embed()

as_wordvec()

Function to load

load_embed()

load_wordvec()

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(): extract word vectors
sum_wordvec(): calculate the sum vector of multiple words
plot_wordvec(): visualize word vectors
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(): compute a similarity matrix of word pairs
plot_similarity(): visualize similarities of word pairs
tab_similarity(): tabulate similarities of word pairs
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(): tokenize raw text
train_wordvec(): train static word embeddings

Pre-trained Language Models (PLM) and Contextualized Word Embeddings

text_init(): set up a Python environment for PLM
text_model_download(): download PLMs from Hugging Face to local “.cache” folder
text_model_remove(): remove PLMs from local “.cache” folder
text_to_vec(): extract contextualized token and text embeddings
text_unmask(): <deprecated> <please use FMAT> fill in the blank mask(s) in a query

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

PsychWordVec

Author

Citation

Installation

Types of Data for `PsychWordVec`

Functions in `PsychWordVec`

PsychWordVec

Author

Citation

Installation

Types of Data for PsychWordVec

Functions in PsychWordVec

Types of Data for `PsychWordVec`

Functions in `PsychWordVec`