rnsb: Relative Negative Sentiment Bias

In sweater: Speedy Word Embedding Association Test and Extras Using R

Relative Negative Sentiment Bias

Description

This function estimate the Relative Negative Sentiment Bias (RNSB) of word embeddings (Sweeney & Najafian, 2 019). If possible, please use query() instead.

Usage

rnsb(w, S_words, A_words, B_words, levels = 1, verbose = FALSE)

Arguments

w	a numeric matrix of word embeddings, e.g. from read_word2vec()
S_words	a character vector of the first set of target words. In an example of studying gender stereotype, it can include occupations such as programmer, engineer, scientists...
A_words	a character vector of the first set of attribute words. In an example of studying gender stereotype, it can include words such as man, male, he, his.
B_words	a character vector of the second set of attribute words. In an example of studying gender stereotype, it can include words such as woman, female, she, her.
levels	levels of entries in a hierarchical dictionary that will be applied (see quanteda::dfm_lookup())
verbose	logical, whether to display information

Value

A list with class "rnsb" containing the following components:

• ⁠$classifer⁠ a logistic regression model with L2 regularization trained with LiblineaR

• ⁠$A_words⁠ the input A_words

• ⁠$B_words⁠ the input B_words

• ⁠$S_words⁠ the input S_words

• ⁠$P⁠ the predicted negative sentiment probabilities rnsb_es() can be used to obtain the effect size of the test.

References

Sweeney, C., & Najafian, M. (2019, July). A transparent framework for evaluating unintended demographic bias in word embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 1662-1667).

Examples

data(googlenews)
S1 <- c("janitor", "statistician", "midwife", "bailiff", "auctioneer",
"photographer", "geologist", "shoemaker", "athlete", "cashier", "dancer",
"housekeeper", "accountant", "physicist", "gardener", "dentist", "weaver",
"blacksmith", "psychologist", "supervisor", "mathematician", "surveyor",
"tailor", "designer", "economist", "mechanic", "laborer", "postmaster",
"broker", "chemist", "librarian", "attendant", "clerical", "musician",
"porter", "scientist", "carpenter", "sailor", "instructor", "sheriff",
"pilot", "inspector", "mason", "baker", "administrator", "architect",
"collector", "operator", "surgeon", "driver", "painter", "conductor",
"nurse", "cook", "engineer", "retired", "sales", "lawyer", "clergy",
"physician", "farmer", "clerk", "manager", "guard", "artist", "smith",
"official", "police", "doctor", "professor", "student", "judge",
"teacher", "author", "secretary", "soldier")
A1 <- c("he", "son", "his", "him", "father", "man", "boy", "himself",
"male", "brother", "sons", "fathers", "men", "boys", "males", "brothers",
"uncle", "uncles", "nephew", "nephews")
B1 <- c("she", "daughter", "hers", "her", "mother", "woman", "girl",
"herself", "female", "sister", "daughters", "mothers", "women", "girls",
"females", "sisters", "aunt", "aunts", "niece", "nieces")
garg_f1 <- rnsb(googlenews, S1, A1, B1)
plot_bias(garg_f1)

sweater documentation built on Nov. 7, 2023, 5:08 p.m.