English colour terms carry gender and valence biases: A corpus study using word embeddings (2024)

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PMC8168888

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PLoS One. 2021; 16(6): e0251559.

Published online 2021 Jun 1. doi:10.1371/journal.pone.0251559

PMCID: PMC8168888

PMID: 34061875

Domicele Jonauskaite, Conceptualization, Visualization, Writing – original draft, Writing – review & editing,^1,^* Adam Sutton, Data curation, Formal analysis, Writing – original draft, Writing – review & editing,² Nello Cristianini, Conceptualization, Data curation, Formal analysis, Supervision, Writing – original draft, Writing – review & editing,² and Christine Mohr, Conceptualization, Supervision, Writing – original draft, Writing – review & editing¹

Søren Wichmann, Editor

Author information Article notes Copyright and License information PMC Disclaimer

Associated Data

Supplementary Materials

Data Availability Statement

Abstract

In Western societies, the stereotype prevails that pink is for girls and blue is for boys. A third possible gendered colour is red. While liked by women, it represents power, stereotypically a masculine characteristic. Empirical studies confirmed such gendered connotations when testing colour-emotion associations or colour preferences in males and females. Furthermore, empirical studies demonstrated that pink is a positive colour, blue is mainly a positive colour, and red is both a positive and a negative colour. Here, we assessed if the same valence and gender connotations appear in widely available written texts (Wikipedia and newswire articles). Using a word embedding method (GloVe), we extracted gender and valence biases for blue, pink, and red, as well as for the remaining basic colour terms from a large English-language corpus containing six billion words. We found and confirmed that pink was biased towards femininity and positivity, and blue was biased towards positivity. We found no strong gender bias for blue, and no strong gender or valence biases for red. For the remaining colour terms, we only found that green, white, and brown were positively biased. Our finding on pink shows that writers of widely available English texts use this colour term to convey femininity. This gendered communication reinforces the notion that results from research studies find their analogue in real word phenomena. Other findings were either consistent or inconsistent with results from research studies. We argue that widely available written texts have biases on their own, because they have been filtered according to context, time, and what is appropriate to be reported.

Introduction

In Western societies, blue is stereotypically associated with boys and pink with girls [1–3]. Curiously enough, these gendered associations were initially arbitrary, but became pervasive in the early 20^th century [1, 2, 4]. Nowadays, many parents continue choosing pink when dressing their daughters, decorating their rooms [5], or buying them toys [6]. Such an upbringing might explain why young girls show a particular liking for pink [7, 8]. In contrast, young adult women choose other colours as their favourite, with blue and red being the most common choices [9]. We explain these differences in colour preferences through gendered and valenced stereotypes.

Empirical studies, focused on colour preferences and colour connotations, have demonstrated that pink is considered to be a feminine colour and blue a masculine colour [3, 8, 10, 11]. Pink further represents groups of low social power and low social status [12–14]. Accordingly, adult women might shun pink to avoid being associated with these representations [9]. Red, on the contrary, represents being in power, dominant, and of high social status [15–18]. These representations potentially explain why adult women like red [9, 19–22] and why red carries both positive and negative connotations [23–27]. When it comes to valence, pink and blue both have been associated with mainly positive emotions [24, 27–29], although blue has been also associated with sadness [30–32].

When considering such gendered colours, we might first think about seeing them on girls’ and boys’ clothing, in their rooms, or their toys. Colours, however, also exist conceptually, in our minds. Colours might be gendered simply because of how we label them. If we think about pink, its gendered connotations might emerge not only because pink repeatedly occurs in feminine contexts visually, but also because it co-occurs with other feminine words in languages (e.g., pink is a girly colour). Empirical studies confirmed that colours expressed through language carry similar connotations to the same colours presented visually. Numerous studies demonstrated the red-attractiveness effect, wherein a person wearing red (man or woman) is perceived as more attractive by an opposite sex individual (for a meta-analysis, see [33]). Important here, simply mentioning that a man was wearing a red shirt had a similar effect on his attractiveness [34]. Though not focussing on the gender-loadings of colours, Jonauskaite and colleagues [35, 36] confirmed that colours, whether presented perceptually (colour patches) or semantically (colour terms), were associated with similar emotions. In the end, if the literature on embodiment and psycholinguistics holds true, an abstract meaning of a word and its respective physical representation in the world should converge on a common cognitive representation [37, 38].

We wanted to learn whether above empirical results on gendered colours can, indeed, be observed when looking at gender and valence biases in widely available written texts. Thus, we analysed gender and valence biases of colour terms in an English corpus, composed of newswire and Wikipedia articles. The latter sources both reflect and shape current standards of language use, as they are co-authored by several people and aimed at vast audiences. To extract biases, we used an artificial intelligence algorithm, focused on natural language processing (NLP). A contemporary key technique in NLP is that of word embeddings (e.g., GloVe, [39], word2vec, [40]), in which a statistical algorithm computes coordinates in a high dimensional space for each word on the basis of a reference corpus. Such NLP algorithms generally learn these coordinates from patterns of word co-occurrences in sentences. These algorithms extract not only semantic and syntactic information from everyday language, but also subtle biases in the usage of words [41]. For example, the words nurse or housekeeper frequently take a more feminine position in the semantic space than the words pilot or engineer, which take a more masculine position [41, 42].

In the current study, we used word embeddings generated from a corpus of 6 billion words by GloVe [39]. We focused on 11 basic colour terms, which included the key terms pink, blue, and red. Using these embeddings, we were able to score 100,000 most frequent words in the corpus in terms of similarities to the concepts of male, female, posemo, and negemo. The concepts of posemo and negemo, respectively, denote lists of positively and negatively laden words (see [43]). Words in our corpus that were closer to the list of words denoting each of the four concept [43] had higher similarity scores (also see, [44]). Afterwards, we computed gender and valence biases. We defined the gender bias as the difference between word similarities to the concepts of male and female, and the valence bias as the difference between word similarities to the concepts of posemo and negemo. We interpreted these biases of colour terms in comparison to i) four anchor words with clear biases (i.e., happy, sad, nun, priest); and ii) a normative word population, namely the 100,000 most frequent words in the corpus. We hypothesised that the word pink would be biased towards femininity and positivity, while the word blue would be biased towards masculinity and positivity. However, blue might be less strongly gender-biased than pink [9]. We expected red to be embedded in both positive and negative contexts, pushing its valence bias towards zero. For its gender bias, we assumed red to represent power, and informed by the literature on gender stereotypes, power would represent masculinity.

Method

We analysed gender and valence biases of 11 British English basic colour terms, namely red, orange, yellow, green, blue, purple, pink, brown, grey, white, and black [45, 46]. We also included four anchor words–two for the valence extremes (happy, sad) and two for the gender extremes (priest, nun). These words acted as sanity checks.

Word embeddings

Each word within a word embedding is represented by a vector w of high dimensionality d (d = 300 in our study). Cosine similarity and Euclidean distances have shown the ability to represent semantic relationships, known as linear substructures, between words (e.g., GloVe, [39]). For example, vector representations for the words man, woman, king, and queen are such that:

$k i n g - q u e e n \approx m a n - w o m a n$

We used a set of pre-trained word embeddings provided by GloVe [39] and available on their website (https://nlp.stanford.edu/projects/glove/). These word embeddings were trained on a corpus, formed by Wikipedia articles, downloaded in 2014, and the Gigaword5 corpus, a large archive of newswire text data collected between 1994 and 2010 [47]. This corpus contains 6 billion word-tokens in total and 400,000 unique words.

Word similarities to concepts

We built on the method by Caliskan and colleagues [41], scoring word similarities to concepts (also see [42]). These concepts were validated by the LIWC project [43], which was created for use in social, clinical, and cognitive psychology and was based on carefully crafted and validated word lists denoting various concepts (e.g., work, family, time, etc.). We used the LIWC 2015 version [48].

In the current study, we used three LIWC word lists [48] representing four relevant concepts: i) “heshe” list, which is split to the “he” and “she” lists, ii) the “posemo” list, and iii) the “negemo” list. The “he” and “she” lists were used to establish similarities to the concepts of male and female, respectively. We acknowledge that gender is non-binary. However, we treated gender for this dataset as such, because we relied on pre-existing data, which separates gender in a binary fashion (i.e., “heshe” list of LIWC). These lists contained words like he, his, him (male), and she, her (female). The “posemo” list contained positively laden words like love, nice, sweet, while the “negemo” list contained negatively laden words like hurt, ugly, nasty.

These word lists were used to generate a “mean vector” (denoted μ) for each concept as defined by:

$μ = \frac{1}{| L |} \sum_{i}^{| L |} {word}_{i}$

(1)

where L is the set of all the words in a given LIWC list, and word_i is the word vector representation for the i’th word in that list. This mean vector μ stands for the general direction in our embedding space. As we worked with four LIWC concepts, the mean vector has directions corresponding to the concepts of male, female, posemo and negemo.

We scored all words using the following cosine similarity function:

$〈 \hat{w}, \hat{μ} 〉 = \sum_{i = 1}^{d} \frac{w_{i}}{‖ w ‖} \cdot \frac{μ_{i}}{‖ μ ‖}$

(2)

Where w is the word vector representation for a word we wish to score, d is the dimension of the embedding space, and w_i is the i’th coordinate of that word vector. Both vectors are normalised to unit length, meaning that the vector length of both μ and w is 1. The result is a single real number ranging between -1 and 1. The higher the number, the more similar are the words to the concept we are scoring.

Gender and valence biases

To calculate gender biases for each word, we subtracted similarity scores to the concept of female from those to male. Thus, a positive gender bias score indicates a bias towards masculinity and a negative score towards femininity. To calculate the valence bias for each word, we subtracted similarity scores to the concept of negemo from those to posemo. Thus, a positive valence bias score indicates that a word is biased towards positivity and a negative score towards negativity. This scoring function is defined by:

$F (w, μ 1, μ 2) = 〈 w, μ 1 〉 - 〈 w, μ 2 〉$

(3)

For bias calculations, scores can range from -2 to 2. The extreme scores would occur when a given word is identical to one vector mean (e.g., posemo) and the opposite to another vector mean (e.g., negemo).

Data analysis

In order to appreciate the magnitude of each bias, we compared gender and valence biases of each colour term with the distribution of biases of a normative word population. The normative word population consisted of the 100,000 most frequent words in the same corpus. We defined extreme similarities to concepts and extreme biases if the scores were below the 5^th and above the 95^th percentile of the normative word population.

We uploaded the code and data for these results to the following GitHub repository; https://github.com/adam-sutton-1992/English-colour-terms-carry-gender-and-valence-biases-A-corpus-study-using-word-embeddings.

Results

Gender bias

We first interpreted word similarities to the concepts of male and female (see Fig 1 and Table 1). Seven colour terms red, blue, green, yellow, brown, white, and black and the anchor words priest and happy had higher similarities to the concept of male than 95% of the words in the normative word population (Fig 1A). Seven colour terms blue, green, pink, brown, white, black, purple and all anchor words had higher similarities to the concept of female than 95% of the words in the normative word population (Fig 1B). Notably, four colour terms, green, blue, brown, and black, and the anchor word happy had higher similarity scores to both concepts (i.e., male and female) than 95% of the words in the normative population.

English colour terms carry gender and valence biases: A corpus study using word embeddings (2)

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Fig 1

Gender bias.

Distributions of similarity scores to the concepts of male (A) and female (B) well as gender biases (C) of colour terms, anchor words, and 100,000 most frequent words in our corpus. Colour terms and anchor words are marked with appropriate colours. Dashed lines indicate the 5^th and 95^th percentiles of the distributions (for exact values, see Table 1).

Table 1

Similarity scores and biases.

These are the similarity scores to the concepts of male, female, posemo, and negemo, as well as gender and valence biases of 11 colour terms and four anchor words. The position of each score in relation to the normative word population (i.e., 100,000 most frequent words) appears under “percentile”. Values in bold are below the 5th or above the 95th percentile, indicating extreme similarities or biases of these words (same data as in Figs Figs11 and and22).

	Male		Female		Gender		Posemo		Negemo		Valence
Word	Similarity	Percentile	Similarity	Percentile	Bias	Percentile	Similarity	Percentile	Similarity	Percentile	Bias	Percentile
Red	0.2531	97.29	0.1622	94.71	0.0909	90.83	0.2659	96.79	0.1639	91.63	0.1019	94.83
Orange	0.1192	87.23	0.1441	93.06	-0.0249	27.49	0.1326	89.11	0.0444	60.41	0.0882	93.04
Yellow	0.1985	95.31	0.1613	94.65	0.0372	66.73	0.1819	93.24	0.1276	87.50	0.0543	85.02
Green	0.2909	98.18	0.2194	97.55	0.0714	84.28	0.2652	96.77	0.0927	79.86	0.1726	98.83
Blue	0.2250	96.41	0.1982	96.74	0.0268	60.05	0.2690	96.87	0.0883	78.52	0.1807	99.01
Purple	0.1422	91.03	0.1809	95.88	-0.0387	20.56	0.1819	93.24	0.1009	82.11	0.0810	91.80
Pink	0.1303	89.29	0.2701	98.79	-0.1397	1.77	0.2283	95.51	0.0903	79.17	0.1380	97.56
Brown	0.3538	99.10	0.2680	98.75	0.0858	89.35	0.2419	96.02	0.1385	89.00	0.1034	95.00
White	0.3261	98.78	0.2568	98.55	0.0692	83.42	0.3120	97.85	0.2007	94.04	0.1130	95.76
Grey	0.1072	84.54	0.1427	92.91	-0.0355	22.15	0.0949	82.49	0.0213	48.13	0.0737	90.34
Black	0.2977	98.31	0.2652	98.71	0.0325	63.79	0.2659	96.79	0.2267	95.37	0.0392	79.16
Nun	0.1082	84.79	0.2670	98.73	-0.1588	1.22	0.1024	84.21	0.1143	85.17	-0.0119	48.02
Priest	0.3405	98.94	0.1985	96.76	0.1420	98.47	0.1437	90.35	0.1665	91.83	-0.0227	40.57
Happy	0.3812	99.33	0.3719	99.72	0.0094	48.59	0.7130	99.99	0.4568	99.68	0.2563	99.84
Sad	0.1433	91.17	0.2079	97.14	-0.0646	11.17	0.4151	99.21	0.5475	99.94	-0.1325	4.17

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When interpreting gender biases (Fig 1C), the anchor word priest scored higher than 95% of the words in the normative word population, meaning it was biased towards masculinity. The anchor word nun and the colour term pink scored lower than 95% of the words in the normative word population, meaning they were both biased towards femininity. No other colour terms or anchor words had gender bias scores outside the 5^th and 95^th percentiles of the normative word population, indicating they did not have extreme gender biases (see Table 1). Thus, similarities to individual concepts (i.e., male and female) do not necessarily mean that a word has a gender bias since similarities to both concepts can be positively correlated.

Valence bias

When interpreting word similarities to the concepts of posemo and negemo (see Fig 2 and Table 1), seven colour terms, red, green, blue, pink, brown, white, and black, and two anchor words happy and sad had higher similarities to the concept of posemo than 95% of the words in the normative word population (Fig 2A). The colour term black and the anchor words happy and sad had higher similarities to the concept of negemo than 95% of the words in the normative word population (Fig 2B). Notably, black, happy, and sad had higher similarity scores to both concepts (i.e., posemo and negemo) than 95% of the words in the normative population.

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Fig 2

Valence bias.

Distributions of similarity scores to the concepts of posemo, denoting positively laden words, (A) and negemo, denoting negatively laden words, (B) as well as valence biases (C) of colour terms, anchor words, and 100,000 most frequent words in our corpus. Colour terms and anchor words are marked with appropriate colours. Dashed lines indicate the 5^th and 95^th percentiles of the distributions (for exact values, see Table 1).

When interpreting valence biases (Fig 2C), five colour terms, green, blue, pink, brown, and white, and the anchor word happy scored higher than 95% of the words in the normative word population, meaning they were biased towards positivity. The anchor word sad scored lower than 95% of the words in the normative word population, meaning it was biased towards negativity. No other colour terms or anchor words had valence bias scores outside the 5^th and 95^th percentiles of the normative word population, indicating they did not have extreme valence biases (see Table 1). Thus, similarities to individual concepts (i.e., posemo and negemo) do not necessarily mean that a word has a valence bias since similarities to both concepts can be positively correlated.

Discussion

We investigated whether empirical results on gender and valence biases for colour terms are reflected in an English corpus composed of newswire and Wikipedia articles. Based on previous empirical studies, we expected pink to be biased towards femininity and positivity, and blue towards masculinity and positivity. We expected red to be biased towards masculinity, while having no particular valence bias because red carries both positive and negative connotations [23–27]. To this end, we scored embeddings of the colour terms pink, blue, and red, which we obtained from GloVe [39]. Scoring was done in terms of similarities to the concepts of male, female, posemo and negemo, that means positively and negatively laden words, as defined by the LIWC project [43]. We then computed gender and valence biases as differences between similarities to the concepts of male vs. female, and posemo vs. negemo, respectively. We did the same for the remaining eight basic colour terms, four anchor words (i.e., happy, sad, nun, priest), and the 100,000 most frequent words in the corpus.

First, we checked for extreme similarities of our colour terms to the concepts of male and female. We defined extreme word similarities when a colour term had a higher similarity with a concept than 95% of the 100,000 most frequent words in the corpus (i.e., normative word population). These comparisons revealed that i) red and blue were closer to the concept of male and ii) blue and pink were closer to the concept of female than 95% of words in the normative word population. That meant that blue was close to both concepts, yielding overall no gender bias. When looking at gender biases in comparison to the normative word population, pink was the only colour term with a gender bias. We confirmed its bias towards femininity. Against our predictions, neither blue nor red were biased towards masculinity and no other colour term had a strong gender bias. Hence, we concluded that only pink conveys gender biased information (femininity) in these texts.

Second, we checked for extreme similarities of our colour terms to the concepts of posemo and negemo. Comparisons with the normative word population revealed that colour terms red, blue, and pink were closer to the concept of posemo, while no colour terms of interest were closer to the concept of negemo than 95% of the most frequent words in the normative word population. When looking at valence biases, as predicted, pink and blue were biased towards positivity and red did not have a strong valence bias. Other positively biased colour terms were green, white, and brown, while no colour term was negatively biased.

The femininity bias of pink mirrors previous empirical findings [3, 10, 11, 49, 50]. In contrast, findings on blue did not confirm a masculinity bias in our corpus, unlike previous empirical studies would have suggested [1, 3, 10, 11]. Indeed, blue seems gender-neutral, equally liked by males and females of all ages [9, 51–56]. Blue might turn into a symbol of masculinity, or boyhood, only when paired with pink, which in turn is a symbol of femininity and girliness, because pink is avoided by boys, men, and some adult women (see a more in-depth reasoning in [9, 57]). In fact, a recent study using a Stroop paradigm demonstrated that masculine words written in pink ink were perceived as being more incongruent than feminine words written in blue ink [57]. As for red, we expected, but did not find a masculinity bias, due to its associations with power and dominance [15–18]. Indeed, red is favoured by many women and might be a symbol of femininity [9, 19–22]. Thus, one can argue that red represents both masculine and feminine characteristics resulting in a negligible bias in our dataset.

Findings on valence biases were also congruent with previous empirical results. Positivity biases of pink and blue were compatible with numerous previous studies [15, 24, 27–29, 35, 36], even though blue also carries some negative connotations. Among English speakers, blue is associated with sadness in addition to several positive connotations [27, 30–32]. We did not expect, and did not observe, that red had a strong positivity or negativity bias. In empirical studies, red has been linked to positive emotions like love, joy, and pleasure as well as negative emotions like anger and hate [24, 26, 27, 35, 36, 58, 59].

Beyond our interest in (potentially) gendered colours, we confirmed only some of the previously observed valence biases for the remaining basic colour terms. Positivity biases of green, and white were compatible with previous studies [24, 25, 27, 32, 60]. However, we did not observe a positivity bias of yellow, despite numerous studies reporting associations with joy, happiness, and other positive emotions [24, 27, 29, 35, 59, 61, 62]. Unexpectedly, we observed a positivity bias of the colour term brown. In nearly all previous empirical studies, brown carried negative associations, including associations with disgust and boredom [27, 30, 35, 59]. The association with disgust is likely due to evolutionary important experiences, like rotten food and faeces, supposedly explaining why people do not like brown [52]. Experiences of rotten food and faeces might, however, be taboo subjects in widely available newswires and Wikipedia articles. Rather, people might be mentioning brown in the contexts of coffee and chocolate, both of which are positive experiences for most. Finally, we did not observe negativity biases of black and grey, despite previous studies showing that black is associated with sadness, fear, and death and grey with sadness and disappointment [24, 26, 27, 58, 60].

Our approach to detect biases in written text is methodologically different from former research studies. Maybe, these differences can explain some discrepancies between the current and previous studies. We extracted biases in an English corpus, while previous research usually tested for implicit or explicit colour meaning via individual ratings, associations, or other questioning (e.g., [3, 8, 10, 11, 15, 23–28, 60]). Additionally, when we consider the posemo and negemo lists, their names (positive emotions, negative emotions) might give rise to the idea that these words represent emotions, but most words do not describe emotions per se, but positively or negatively laden concepts (e.g., see definitions of emotions in [63–66]). For instance, sadness, anger, or joy would represent emotions, but not nice, kiss, or sceptical. Differences in definitions of concepts, study material and procedures might lead to varied study results.

Limitations and future directions

In the field of engineering, researchers use word embeddings as input for downstream artificial intelligence tasks. Wikipedia and newswire articles are considered standard sources for learning word embeddings. One could argue that such widely available text sources reflect current standards of language use, and by inference, current thinking, as they are authored by several people and aimed at vast audiences. However, our method has at least two limitations. First, in our case, the text sources were in English, likely written by native and non-native speakers. Therefore, our results should not be generalised to other languages and populations. Second, the method leaves us without socio-demographic information about the writers (e.g., their gender, age, country) or their personalities (but see [67]). In the current design, we cannot know if the reported biases are common to everyone or are rather representative for only a particular part of society or a single culture. To obtain such information, we would need controlled studies. In particular, we would have to be able to link personal, socio-demographic, linguistic, and geographical information to an individual and their behavioural output (see also[27, 68–70]).

Worth noting, neither our corpus study nor empirical studies reflect spontaneous conversations. When setting up research studies, researchers decide beforehand what they wish to test, designing the method accordingly. In the current and other corpus studies, one extracts meaning from large corpora. In our case, we used newswire and Wikipedia articles, conveying information that was likely filtered by topic, and presented in a socially and politically acceptable manner. If we take the terms black and green, the former might dominate discussions around racial and the latter around environmental issues. Furthermore, obvious descriptors might not be mentioned. A writer might not see the necessity to state that grass is green, or faeces are brown. Finally, the contents of corpora might depend on when and where they were published, as topics vary in popularity over time and between places. Most recently, gender issues were again intensively discussed with the prominence of the #MeToo movement.

To understand how colour terms are used in spontaneous language, studies could analyse written communications on social media like Twitter (see an example in [71]) or conduct observational studies. In social media, communications might be more spontaneous, and thus closer to what people think and feel. Yet, these communications have other drawbacks, such as being limited in length, or favouring omissions and incomplete sentences.

Conclusions

We investigated whether pink, blue, and red have gender and valence biases in a written text corpus. With the help of artificial intelligence technology, we could show that pink was the only gendered colour, biased towards femininity. These results show that artificial intelligence can be used to assess how empirical results, often from the laboratory research studies, may relate to how people use colour terms in written texts. For some colours, our corpus study mirrored empirical study results (i.e., pink and femininity, blue and pink and positivity), for others, we observed differences (e.g., brown and positivity). Thus, we argued that written texts not only reflect human thought processes, but yield biases on their own, potentially due to selection in reporting.

Funding Statement

This research was made possible through a Doc.CH fellowship grant to DJ (P0LAP1_175055) and a project-funding grant to CM (100014_182138) from the Swiss National Science Foundation (http://www.snf.ch/en). AS was supported by the Engineering and Physical Sciences Research Council (EP/I028153/ and EP/L016656/1; https://epsrc.ukri.org/). NC was supported by the ERC grant ThinkBIG (https://cordis.europa.eu/project/id/339365).

Data Availability

The pre-existing word embeddings the authors used in this study are available at https://nlp.stanford.edu/projects/glove/. The authors do not own these word embeddings. The authors confirm that researchers can access this data in the same way they did. The authors’ analyses of these word embeddings are reproducible from their own data set which are available at https://github.com/adam-sutton-1992/English-colour-terms-carry-gender-and-valence-biases-A-corpus-study-using-word-embeddings. The authors confirm they had no special access privileges.

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PLoS One. 2021; 16(6): e0251559.

Decision Letter 0

2021; 16(6): e0251559.

Published online 2021 Jun 1. doi:10.1371/journal.pone.0251559.r001

Søren Wichmann, Academic Editor

1 Oct 2020

PONE-D-20-26001

Colour terms carry gender and valence biases in natural language corpora

PLOS ONE

Dear Dr. Jonauskaite,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Søren Wichmann, PhD

Academic Editor

PLOS ONE

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Reviewer #1:Yes

Reviewer #2:Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1:Yes

Reviewer #2:Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1:No

Reviewer #2:Yes

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1:Yes

Reviewer #2:Yes

**********

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Reviewer #1:The reviewer self-identifies as Bodo Winter and is available for follow-up questions. Please see the attached PDF for detailed feedback, which can also be accessed under this link:

http://appliedstatisticsforlinguists.org/PLOS_ONE_gender.pdf

Reviewer #2:This article presents an analysis of the gender and valence bias of color terms through a Glove embeddings model fitted against Wikipedia and news. The authors find results that are consistent with previous results from experiments.

The methodology is appropriate and the presentation is good. However, I have some comments regarding the interpretation of results and some methodological details:

- Results are framed as "in natural language" in general, but are only based on a combination of Wikipedia and news. The authors fairly mention this in their limitations section, but to generalize results to natural language it would be necessary to replicate the results with other corpora and/or languages. As there are pre-trained embeddings for many corpora and LIWC present in a wide variety of languages, it would not be hard for the authors to add replications that support their generalization to natural language. In case this is not possible, the abstract and conclusions of the article can be further contextualized to point that results only apply to one English corpus, without generalizing to natural language.

- Positive and Negative Affect in LIWC are treated as ends of a single dimension of valence, while in LIWC they are conceived as two variables that can co-occur, not just in text but also at the individual level. Even if they are not completely orthogonal, mapping valence as the subspace between PA and NA in the embeddings space induces a situation in which the middle point is not well defined. In the definition of valence in the paper, a word that has the same cosine similarity to the centroid of PA words as to the centroid of NA words is neutral, but this does not have to be the case. If the authors really want to analyze emotional valence, there is a large number of affective norms lexica that would allow them to approximate a mapping from the embeddings space to the dimension of valence as defined in those lexica. If they wanted to study something more general in the lines of evaluative meanings, other lexica like the General Inquirer would be more suitable than LIWC. I recommend the authors to revise the assumption of their calculation of valence bias and revise whether zero in their scale really means a neutral bias.

- The null model used for statistical analysis assumes that gender and valence bias are orthogonal. From previous works in sentiment analysis, we know that this is unlikely to be the case in many models like Glove. This assumed orthogonality can be seen in the Figure, where the limits for significance describe a square. Before concluding what associations are significant and which are not, the authors can use the bivariate distribution of valence and gender bias in their all-pairs sample and test each 2-D point of colors against that more realistic null model. This might very well add power to the method.

- The statistical test assumes normality of the distribution under the null, but the SI figure seems to show quite some deviation from a normal distribution, especially for the case of valence bias (if the lines in the figure are indeed a normal fit, which is not explained). I think the authors do not need this normal assumption at all, they can just use the quantiles of the bivariate distribution composed of the joint values shown in both SI figures, labelling for example which points are beyond the 95% quantiles in their color word analysis.

- On a clarity note, authors should report which version of LIWC they used. They cite the 2001 version, which would be quite outdated compared to the 2015 version. They should also explain how they mapped entries in the LIWC dictionary, especially those with wildcards or other rules, to individual words in the embeddings model.

**********

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Reviewer #1:Yes:Bodo Winter

Reviewer #2:No

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Attachment

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PLoS One. 2021; 16(6): e0251559.

Author response to Decision Letter 0

2021; 16(6): e0251559.

Published online 2021 Jun 1. doi:10.1371/journal.pone.0251559.r002

31 Mar 2021

Our response to reviewers has been attached separately.

Attachment

Submitted filename: Response_letter_PLoS.docx

Click here for additional data file.^{(214K, docx)}

PLoS One. 2021; 16(6): e0251559.

Decision Letter 1

2021; 16(6): e0251559.

Published online 2021 Jun 1. doi:10.1371/journal.pone.0251559.r003

Søren Wichmann, Academic Editor

12 Apr 2021

PONE-D-20-26001R1

English colour terms carry gender and valence biases: A corpus study using word embeddings

PLOS ONE

Dear Dr. Jonauskaite,

Thank you for submitting your revised manuscript to PLOS ONE. You can essentially regard this as accepted, but Reviewer #1 mentions a "tiny thing" that would still merit a bit of revision.

Please submit your revised manuscript by May 27 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at gro.solp@enosolp. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Søren Wichmann, PhD

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1:All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1:Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1:Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1:Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1:Yes

**********

6. Review Comments to the Author

Reviewer #1:Excellent job at addressing my comments from the first round. I also really appreciated the justification for those aspects where the authors stood their ground and argued (in my mind very well) why it makes not that much sense to implement certain changes. I like the new limitations section and the manuscript reads very well overall. I think this is a straightforward accept.

I'd just change one tiny thing: You say: "This should be the case according to theories in cognitive sciences [37,38]." But "Theories in cognitive science" could be anything or nothing. It's exceedingly vague and I think you can be more specific here about what sort of theories these are? As there is a lot of theoretical diversity WITHIN cognitive science, I could easily see some cognitive scientists been thrown off by such a broad sweep statement. Anyway, this is all but a minor fix.

Thank you for submitting a great paper and also for your thoughtful reviewer response.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1:Yes:Bodo Winter

PLoS One. 2021; 16(6): e0251559.

Author response to Decision Letter 1

2021; 16(6): e0251559.

Published online 2021 Jun 1. doi:10.1371/journal.pone.0251559.r004

28 Apr 2021

We have adapted the problematic sentence in the introduction as suggested by Dr Winter.

PLoS One. 2021; 16(6): e0251559.

Decision Letter 2

2021; 16(6): e0251559.

Published online 2021 Jun 1. doi:10.1371/journal.pone.0251559.r005

Søren Wichmann, Academic Editor

29 Apr 2021

English colour terms carry gender and valence biases: A corpus study using word embeddings

PONE-D-20-26001R2

Dear Dr. Jonauskaite,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Note there is a typo in added sentence "if the literature on embodiment and psycholinguistics hold true, " (hold -> holds). You can correct that in the proofs.

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PLoS One. 2021; 16(6): e0251559.

Acceptance letter

2021; 16(6): e0251559.

Published online 2021 Jun 1. doi:10.1371/journal.pone.0251559.r006

Søren Wichmann, Academic Editor

21 May 2021

PONE-D-20-26001R2

English colour terms carry gender and valence biases: A corpus study using word embeddings

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