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.
Citation: Jonauskaite D, Sutton A, Cristianini N, Mohr C (2021) English colour terms carry gender and valence biases: A corpus study using word embeddings. PLoS ONE 16(6): e0251559. https://doi.org/10.1371/journal.pone.0251559
Editor: Søren Wichmann, Leiden University, NETHERLANDS
Received: August 19, 2020; Accepted: April 29, 2021; Published: June 1, 2021
Copyright: © 2021 Jonauskaite et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
Funding: 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).
Competing interests: The authors have declared that no competing interests exist.
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 20th century [1, 2, 4]. Nowadays, many parents continue choosing pink when dressing their daughters, decorating their rooms , or buying them toys . 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 . 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 . 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 ). Important here, simply mentioning that a man was wearing a red shirt had a similar effect on his attractiveness . 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, , word2vec, ), 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 . 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 . 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 ). Words in our corpus that were closer to the list of words denoting each of the four concept  had higher similarity scores (also see, ). 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 . 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.
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.
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, ). For example, vector representations for the words man, woman, king, and queen are such that:
We used a set of pre-trained word embeddings provided by GloVe  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 . 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 , scoring word similarities to concepts (also see ). These concepts were validated by the LIWC project , 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 .
In the current study, we used three LIWC word lists  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: (1) where L is the set of all the words in a given LIWC list, and wordi 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.
Where w is the word vector representation for a word we wish to score, d is the dimension of the embedding space, and wi 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: (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).
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 5th and above the 95th 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.
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.
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 5th and 95th percentiles of the distributions (for exact values, see Table 1).
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 1 and 2).
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 5th and 95th 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.
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.
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 5th and 95th 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 5th and 95th 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.
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 . 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 . 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 . 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 . 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 ). 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 ) 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.
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.
- 1. Del Giudice M. The twentieth century reversal of pink-blue gender coding: A scientific urban legend? Arch Sex Behav. 2012;41: 1321–1323. pmid:22821170
- 2. Del Giudice M. Pink, blue, and gender: An update. Arch Sex Behav. 2017;46: 1555–1563. pmid:28664466
- 3. Cunningham SJ, Macrae CN. The colour of gender stereotyping. Br J Psychol. 2011;102: 598–614. pmid:21752009
- 4. Frassanito P, Pettorini B. Pink and blue: The color of gender. Child’s Nerv Syst. 2008;24: 881–882. pmid:18176808
- 5. Pomerleau A, Bolduc D, Malcuit G, Cossette L. Pink or blue: Environmental gender stereotypes in the first two years of life. Sex Roles. 1990;22: 359–367.
- 6. Auster CJ, Mansbach CS. The gender marketing of toys: An analysis of color and type of toy on the Disney store website. Sex Roles. 2012;67: 375–388.
- 7. LoBue V, DeLoache JS. Pretty in pink: The early development of gender-stereotyped colour preferences. Br J Dev Psychol. 2011;29: 656–667. pmid:21848751
- 8. Wong WI, Hines M. Preferences for pink and blue: The development of color preferences as a distinct gender-typed behavior in toddlers. Arch Sex Behav. 2015;44: 1243–1254. pmid:25680819
- 9. Jonauskaite D, Dael N, Chèvre L, Althaus B, Tremea A, Charalambides L, et al. Pink for girls, red for boys, and blue for both genders: Colour preferences in children and adults. Sex Roles. 2019;80: 630–642.
- 10. Chen Y, Yang J, Pan Q, Vazirian M, Westland S. A method for exploring word-colour associations. Color Res Appl. 2020;45: 85–94.
- 11. Lucassen MP, Gevers T, Gijsenij A. Texture affects color emotion. Color Res Appl. 2011;36: 426–436.
- 12. Ben-Zeev A, Dennehy TC. When boys wear pink: A gendered color cue violation evokes risk taking. Psychol Men Masc. 2014;15: 486–489.
- 13. Hughes HL. Pink tourism: holidays of gay men and lesbians. CABI; 2006. https://doi.org/10.1079/9781845930769.0000
- 14. Koller V. “Not just a colour”: Pink as a gender and sexuality marker in visual communication. Vis Commun. 2008;7: 395–423.
- 15. Adams FM, Osgood CE. A cross-cultural study of the affective meanings of color. J Cross Cult Psychol. 1973;4: 135–157.
- 16. Soriano C, Valenzuela J. Emotion and colour across languages: implicit associations in Spanish colour terms. Soc Sci Inf. 2009;48: 421–445.
- 17. Mentzel S V., Schücker L, Hagemann N, Strauss B. Emotionality of colors: An implicit link between red and dominance. Front Psychol. 2017;8: 6–11. pmid:28321202
- 18. Wu Y, Lu J, van Dijk E, Li H, Schnall S. The color red is implicitly associated with social status in the United Kingdom and China. Front Psychol. 2018;9: 1–8. pmid:29410639
- 19. Hurlbert AC, Ling Y. Biological components of sex differences in color preference. Curr Biol. 2007;17: 623–625. pmid:17714645
- 20. Sorokowski P, Sorokowska A, Witzel C. Sex differences in color preferences transcend extreme differences in culture and ecology. Psychon Bull Rev. 2014;21: 1195–201. pmid:24570324
- 21. Witzel C. Commentary: An experimental study of gender and cultural differences in hue preferences. Front Psychol. 2015;6: 1–3. pmid:25688217
- 22. Al-Rasheed AS. An experimental study of gender and cultural differences in hue preference. Front Psychol. 2015;6: 1–5. pmid:25688217
- 23. Sutton TM, Altarriba J. Color associations to emotion and emotion-laden words: A collection of norms for stimulus construction and selection. Behav Res Methods. 2016;48: 686–728. pmid:25987304
- 24. Kaya N, Epps HH. Relationship between color and emotion: a study of college students. Coll Stud J. 2004;38: 396–406. https://psycnet.apa.org/record/2004-19149-009
- 25. Fugate JMB, Franco CL. What color is your anger? Assessing color-emotion pairings in English speakers. Front Psychol. 2019;10: 1–17. pmid:30713512
- 26. Wexner LB. The degree to which colors (hues) are associated with mood-tones. J Appl Psychol. 1954;38: 432–435.
- 27. Jonauskaite D, Abu-Akel A, Dael N, Oberfeld D, Abdel-Khalek AM, Al-Rasheed AS, et al. Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity. Psychol Sci. 2020;31: 1245–1260. pmid:32900287
- 28. Valdez P, Mehrabian A. Effects of color on emotions. J Exp Psychol Gen. 1994;123: 394–409. pmid:7996122
- 29. Schloss KB, Witzel C, Lai LY. Blue hues don’t bring the blues: questioning conventional notions of color–emotion associations. J Opt Soc Am A. 2020;37: 813. pmid:32400715
- 30. Sandford JL. Turn a colour with emotion: a linguistic construction of colour in English. J Int Colour Assoc. 2014;13: 67–83. https://psycnet.apa.org/record/2004-19149-009
- 31. Barchard KA, Grob KE, Roe MJ. Is sadness blue? The problem of using figurative language for emotions on psychological tests. Behav Res Methods. 2017;49: 443–456. pmid:26936461
- 32. Hanada M. Correspondence analysis of color–emotion associations. Color Res Appl. 2018;43: 224–237.
- 33. Lehmann GK, Elliot AJ, Calin-Jageman RJ. Meta-analysis of the effect of red on perceived attractiveness. Evol Psychol. 2018;16: 1–27. pmid:30282470
- 34. Pazda AD, Elliot AJ. Processing the word red can enhance women’s perceptions of men’s attractiveness. Curr Psychol. 2017;36: 316–323.
- 35. Jonauskaite D, Parraga CA, Quiblier M, Mohr C. Feeling blue or seeing red? Similar patterns of emotion associations with colour patches and colour terms. I-Perception. 2020;11: 1–24. pmid:32117561
- 36. Jonauskaite D, Camenzind L, Parraga CA, Diouf CN, Mercapide Ducommun M, Müller L, et al. Colour-emotion associations in individuals with red-green colour blindness. PeerJ. 2021;9: e11180. pmid:33868822
- 37. Lupyan G, Winter B. Language is more abstract than you think, or, why aren’t languages more iconic? Philos Trans R Soc B Biol Sci. 2018;373: 20170137. pmid:29915005
- 38. Louwerse MM. Symbol interdependency in symbolic and embodied cognition. Top Cogn Sci. 2011;3: 273–302. pmid:25164297
- 39. Pennington J, Socher R, Manning C. Glove: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics; 2014. pp. 1532–1543. https://doi.org/10.3115/v1/D14-1162
- 40. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. 1st Int Conf Learn Represent ICLR 2013—Work Track Proc. 2013; 1–12. Available: http://arxiv.org/abs/1301.3781
- 41. Caliskan A, Bryson JJ, Narayanan A. Semantics derived automatically from language corpora contain human-like biases. Science (80-). 2017;356: 183–186. pmid:28408601
- 42. Sutton A, Lansdall-Welfare T, Cristianini N. Biased embeddings from wild data: Measuring, understanding and removing. In: Duivesteijn W, Siebes A, Ukkonen A, editors. Advances in Intelligent Data Analysis XVII IDA 2018 Lecture Notes in Computer Science, vol 11191. Springer; 2018. pp. 328–339. https://doi.org/10.1007/978-3-030-01768-2_27
- 43. Pennebaker JW, Francis ME, Booth RJ. Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates; 2001.
- 44. Sutton A, Cristianini N. On the learnability of concepts. In: Maglogiannis I, Iliadis L, Pimenidis E, editors. Artificial Intelligence Applications and Innovations AIAI 2020 IFIP Advances in Information and Communication Technology, vol 584. Cham: Springer; 2020. pp. 420–432.
- 45. Berlin B, Kay P. Basic color terms. Their universality and evolution. Berkeley, Los Angeles, Oxford: University of California Press; 1969.
- 46. Lindsey DT, Brown AM. The color lexicon of American English. J Vis. 2014;14: 1–25. pmid:24569983
- 47. Parker R, Graff D, Kong J, Chen K, Maeda K. English gigaword fifth edition. Linguistic Data Consortium; 2011.
- 48. Pennebaker J, Booth R, Boyd R, Francis M. Linguistic Inquiry and Word Count: LIWC2015. 2015. Available: https://s3-us-west-2.amazonaws.com/downloads.liwc.net/LIWC2015_OperatorManual.pdf
- 49. Clarke T, Costall A. The emotional connotations of color: A qualitative investigation. Color Res Appl. 2008;33: 406–410.
- 50. Koller V. “Not just a colour”: Pink as a gender and sexuality marker in visual communication. Vis Commun. 2008;7: 395–423.
- 51. Jonauskaite D, Mohr C, Antonietti J-P, Spiers PM, Althaus B, Anil S, et al. Most and least preferred colours differ according to object context: New insights from an unrestricted colour range. Cropper SJ, editor. PLoS One. 2016;11: e0152194. pmid:27022909
- 52. Palmer SE, Schloss KB. An ecological valence theory of human color preference. Proc Natl Acad Sci. 2010;107: 8877–8882. pmid:20421475
- 53. Eysenck HJ. A critical and experimental study of colour preferences. Am J Psychol. 1941;54: 385–394.
- 54. Fortmann-Roe S. Effects of hue, saturation, and brightness on color preference in social networks: Gender-based color preference on the social networking site Twitter. Color Res Appl. 2011;38: 196–202.
- 55. Skelton AE, Franklin A. Infants look longer at colours that adults like when colours are highly saturated. Psychon Bull Rev. 2019;1897. pmid:31848908
- 56. Taylor C, Schloss K, Palmer SE, Franklin A. Color preferences in infants and adults are different. Psychon Bull Rev. 2013;20: 916–22. pmid:23435629
- 57. Li Y, Du J, Song Q, Wu S, Liu L. An ERP Study of the Temporal Course of Gender–Color Stroop Effect. Front Psychol. 2021;11: 1–9. pmid:33519635
- 58. Mohammad S. Colorful Language: Measuring Word-Color Association. 2nd Work Cogn Model Comput Linguist. 2011; 97–106. Available: www.aclweb.org/anthology/W11-0611
- 59. Fugate JMB, Franco CL. What Color Is Your Anger? Assessing Color-Emotion Pairings in English Speakers. Front Psychol. 2019;10. pmid:30863330
- 60. Warriner AB, Kuperman V, Brysbaert M. Norms of valence, arousal, and dominance for 13,915 English lemmas. Behav Res Methods. 2013;45 VN-r: 1191–1207. pmid:23404613
- 61. Jonauskaite D, Abdel-Khalek AM, Abu-Akel A, Al-Rasheed AS, Antonietti J-P, Ásgeirsson ÁG, et al. The sun is no fun without rain: Physical environments affect how we feel about yellow across 55 countries. J Environ Psychol. 2019;66: 101350.
- 62. Palmer SE, Schloss KB, Xu Z, Prado-Leon LR. Music-color associations are mediated by emotion. Proc Natl Acad Sci. 2013;110: 8836–8841. pmid:23671106
- 63. Scherer KR. What are emotions? And how can they be measured? Soc Sci Inf. 2005;44: 695–729.
- 64. Dixon T. Emotion: One word, many concepts. Emot Rev. 2012;4: 387–388.
- 65. Mulligan K, Scherer KR. Toward a working definition of emotion. Emot Rev. 2012;4: 345–357.
- 66. Kleinginna PR, Kleinginna AM. A categorized list of motivation definitions, with a suggestion for a consensual definition. Motiv Emot. 1981;5: 263–291.
- 67. Jadin T, Gnambs T, Batinic B. Personality traits and knowledge sharing in online communities. Comput Human Behav. 2013;29: 210–216.
- 68. Mehr SA, Singh M, Knox D, Ketter DM, Pickens-Jones D, Atwood S, et al. Universality and diversity in human song. Science (80-). 2019;366: eaax0868. pmid:31753969
- 69. Cowen AS, Elfenbein HA, Laukka P, Keltner D. Mapping 24 Emotions Conveyed by Brief Human Vocalization. Am Psychol. 2018;74: 698–712. pmid:30570267
- 70. Cowen AS, Fang X, Sauter D, Keltner D. What music makes us feel: At least 13 dimensions organize subjective experiences associated with music across different cultures. Proc Natl Acad Sci. 2020;117: 1924–1934. pmid:31907316
- 71. Dzogang F, Lightman S, Cristianini N. Diurnal variations of psychometric indicators in Twitter content. PLoS One. 2018;13: e0197002. pmid:29924814