Japanese Word–Color Associations with and without Contexts
Jun Harashima
Cookpad Inc.
Yebisu Garden Place Tower 12F, 4-20-3 Ebisu, Shibuya-ku, Tokyo, 150-6012, Japan
jun-harashima@cookpad.com
Abstract
Although some words carry strong associations with specific colors (e.g., the word danger is associated with the color red), few studies
have investigated these relationships. This may be due to the relative rarity of databases that contain large quantities of such information.
Additionally, these resources are often limited to particular languages, such as English. Moreover, the existing resources often do notconsider the possible contexts of words in assessing the associations between a word and a color. As a result, the influence of context
on word–color associations is not fully understood. In this study, we constructed a novel language resource for word–color associations.
The resource has two characteristics: First, our resource is the first to include Japanese word–color associations, which were collected
via crowdsourcing. Second, the word–color associations in the resource are linked to contexts. We show that word–color associations
depend on language and that associations with certain colors are affected by context information.
Keywords: word–color associations, contexts

1. Introduction

2.

Some words are associated with colors (i.e., danger andred) and these associations play important roles in information visualization. For example, red is used to successfully
strengthen the message given by stop signs (i.e., danger).
However, few studies have investigated word–color associations. This is because few available resources include awide range of word–color associations. Additionally, such
resources are often limited to particular languages (e.g., English).
Moreover, the word–color associations included in the existing resources are generally not linked to any contexts.
Consequently, in assessing the associations between a word
and a color, previous studies may not have considered thepossible contexts of the word (Mohammad, 2011; Volkova
et al., 2012; Lafourcade et al., 2014). Thus, the influence ofcontext on word–color associations is not fully understood.
Our study represents two main contributions to linguistic
research: The first is the construction of the initial resourcefor exploring Japanese word–color associations. As described above, the existing resources are limited to specific languages. We collected word–color associations inJapanese with the goal of making them available to researchers engaged in word–color association research.
The second contribution of this report is the communication of our findings regarding the effects of context information on word–color associations. As in previous studies, we asked participants to name a color that they associated with a presented word. We presented each word withseveral contexts and collected multiple word–color associations for each context. We compared the associations obtained for words with and without contexts to examine howcontext affects word–color associations.
The remainder of this paper is organized as follows. Section 2. briefly describes relevant previous studies. Section 3. explains how we collected Japanese word–color associations with and without contexts. Section 4. reports ourfindings based on the collected data, and our conclusions
are presented in Section 5..

Related WorkAs described in the previous section, few studies have investigated word–color associations. To the best of ourknowledge, the following are the only studies to explicitlyand comprehensively focus on word–color associations.
Ozbal et al. (2011) proposed several processes that can beused to automatically associate words and colors. These
are based on image analysis, language models, and latentsemantic analysis. The researchers found that a method employing the distribution of colors in images retrieved fromthe web produced the best results.
Mohammad (2011) constructed a word–color association
lexicon for English using crowdsourcing. The lexicon consists of 8, 813 words with associated colors that have been
selected by participants from 11 basic colors. His lexicon
is the first large-scale publicly available word–color association resource.
Volkova et al. (2012) also used crowdsourcing to construct
an English lexicon of word–color–emotion associations.
Unlike Mohammad, the researchers did not place any restrictions on the number of annotated colors. As a result,
their lexicon has a large number of colors (2, 315 colors).
Additionally, they showed that words associated with a particular color may express the same sentiment as the color.
Lafourcade et al. (2014) constructed the first resource forFrench word–color associations. The researchers implemented a game in which players were asked to identify acolor that he/she associated with a given word. They wereable to collect more than 15, 000 words that were associatedwith one or more colors.
Our work differs from the above-mentioned efforts in that
we constructed a resource for Japanese word–color associations. As described in Section 3., we collected word–color
associations for 2, 903 Japanese words. Additionally, weinvestigated the ways in which the context of a word affects its corresponding color associations. As described inSection 4., we show that associations with certain colors arestrongly affected by context information.

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. . . ボールを相手に渡す危険性が常につきまとう . . . (. . . it is dangerous if they drop the ball . . . )red: 5, yellow: 4, purple: 1, orange: 1. . . 危険なことをトライさせて見物するという . . . (. . . program that places the cast members in danger . . . )red: 4, yellow: 4, gray: 2, pink: 1. . . 危険を感じれば引き返すのが山のプロの . . . (Mountain climbers turn back when they sense danger . . . )red: 6, black: 2, gray: 2, green: 1

Figure 1: Word–color associations with contexts of 危険 (danger).

危険 (danger)
red: 7, yellow: 3, black: 1

Figure 2: Word–color associations without contexts of 危険 (danger).

3. Data Collection

3.3. Basic Colors
In our resource, we take account of the following 11 basiccolors:

3.1. Data Format
In this study, we collected Japanese word–color associations with and without contexts. Figure 1 shows examplesof the color associations given for the different contexts. In
our resource, three contexts are given for each word andcolor associations are given for each context. The values
in the figure correspond to the number of participants whoassociated the color with the word in the given context. For
contexts, we randomly chose sentences containing the target words from the Kyoto University Text Corpus (Kurohashi and Nagao, 1998), which is one of the most wellknown parsed corpus in Japanese. Although the figure onlyshows parts of the sentences, we presented the complete
sentences when we collected the associations from participants (see also Figure 3).
Figure 2 gives examples of the associations that were reported for a word that was presented without context. The
values represent the number of participants who annotatedthe color to the word without a context. We used the sameformat as that in existing resources (Mohammad, 2011;
Volkova et al., 2012; Lafourcade et al., 2014).

3.2. Target Words
We evaluated a total of 2, 903 Japanese words. We extracted all substantives with a frequency of greater than
three in the Kyoto University Text Corpus. As substantives, we selected common nouns, sahen nouns, the stemsof na-adjectives, and the stems of nano-adjectives. Although na-adjectives and nano-adjectives are called adjectives, we did not discriminate between the stems of thesewords and nouns in this study. This is because there is noclear distinction between these two categories in Japanese.
As described in the previous section, for each word, we extracted three sentences containing that word for context.

1. white, 2. black, 3. red, 4. green, 5. yellow,
6. blue, 7. brown, 8. pink, 9. purple, 10. orange,
and 11. gray,as proposed by Berlin and Kay (1969). These colors
are also used in a previous English resource (Mohammad,2011).

3.4. Collection Process
As in previous studies, we used crowdsourcing to collect
word–color associations. More precisely, we used lancers,1which is one of the biggest crowdsourcing sites in Japan.
To collect color associations for each word in context, wepresented participants with the context sentence and our 11
basic color names. Then, we asked them to select one colorthat they associated with the word. The order of the color
names was randomized for each participant. A total of 33
participants assessed each word, i.e., 11 for each context.
Finally, we obtained 95, 799 associations from 468 participants (11 color associations for 2, 903 words with theirthree contexts each).
Like Mohammad (2011), we did not provide a “not associated with any color” option. If there were no associations
between a word and a color, we expected a low level ofagreement between the collected colors for that word. Ideally, such words would have one association for each color
because the participants would arbitrarily select one color
from the 11 basic colors.
To collect word–color associations without contexts, wepresented the words individually and asked the participants
to select one color that they associated with each word.
Note that the participants who viewed words without contexts were different than those who viewed words with contexts. The other conditions were identical to those for col-2120

1

www.lancers.jp

Figure 3: A screen shot of the data collection interface.

Table 1: Percentage of words associated with each color.
Japanese (with contexts)
Japanese (without contexts)
English (without contexts)

white
14.9
20.2
22.7

black
12.9
12.3
18.4

red
16.1
15.4
13.4

green
7.0
7.7
12.1

yellow
8.5
7.8
10.0

blue
15.6
14.3
6.4

brown
5.4
5.6
6.3

pink
2.3
2.8
5.3

purple1.8
1.3
2.1orange
3.6
2.7
1.5

gray
11.9
10.1
1.3

lecting associations with contexts. That is, the order ofthe color names was randomized and 11 color associations
were obtained (see also Figure 2).

are somewhat reliable.

3.5. Crowdsourcing Details

4.1. Effects of Context Information

In the final part of this section, we describe the details of
our crowdsourcing process. In this study, participants wererecruited on the lancers website, as described above. We
uploaded our tasks to the site and people who were interested in the tasks completed the annotations.
Although we did not explicitly verify that all of the participants were native Japanese speakers, we believe that thiswas the case for most or all of the participants because thesite is written in Japanese and is targeted at Japanese people. A good understanding of Japanese is required to navigate the website.
We paid the participants one Japanese yen for each annotation. For example, if a participant provided colors for fivewords, he/she obtained five yen for the annotations. Notethat for increased convenience, we presented five words
with contexts simultaneously to participants, as shown in
Figure 3. This was also the case for the collection of color
associations for words without contexts.
We were not able to confirm that the participants did not
simply choose color associations in a random fashion.
However, because the annotation results fit our hypothesis(see also Tables 2 and 3 in the next section), we think they

Table 1 gives the percentage of words that were associated with each color. The first and second rows give thepercentages for associations with and without contexts, respectively. We calculated the values according to the colors
that were selected by the majority of participants for eachword. To calculate the percentage of color associations forwords with contexts, we randomly chose one context foreach word.
From the two rows, it is clear that there are differences in
the percentages of associations for some colors. For example, the percentage of white decreased by more than 5% andthat of blue and gray increased by more than 1% when contexts were considered. Conversely, the differences betweenconditions for the rest of the colors were small. Based onthese results, we suggest that associations with certain colors are strongly affected by context information.
We also investigated how strongly a word was associatedwith a color depending on whether the word was presented
with or without a context. As described in Section 3.4., if
there is no association between a word and a specific color,the distribution of colors selected should be uniform. In ourinvestigation, the number of words with distributions that4.

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Data Analysis

Table 2: Words that were strongly associated with colors (with contexts)# of votes
111098765white積雪 (snow)
もち (rice cake)
ガラス (glass)
ＨＬＡ (HLA)
自宅 (one’s home)
骨髄 (marrow)
国際 (international)

blacknone
ピアノ (piano)
罪 (sin)
野良黒 (Norakuro)
死 (death)
閉鎖 (shut-down)
文字 (character)red
爆発 (explosion)
トマト (tomato)
燃料 (fuel)
出費 (expenses)
急 (urgency)
赤ちゃん (baby)
減益 (drop in earnings)

green
庭園 (garden)
環境 (environment)
葉 (leaf)
入山 (entering a mountain)
生物 (living thing)
ゴルフ (golf)
豊か (rich)# of votes
11
10
98
7
65

yellownone
落雷 (thunderbolt)
トランペット (trumpet)
賞金 (prize)
星 (star)宝飾 (jewelry)
対照 (contrast)

bluenone
進水 (launching)
空港 (airport)
上空 (the sky)
世界 (world)
高速 (high speed)
画像 (image)

brown
none
サル (monkey)
いかだ (raft)
将棋 (shogi)
土 (soil)
種 (seed)レンガ (brick)

pink
none
女の子 (girl)none
娘 (daughter)
妹 (sister)
交際 (romance)
喜び (pleasure)# of votes
11
10
9
8
7
6
5

purple
none
none
none
紫 (purple)
none
none
誘致 (invitation)orange
none
none
none
none
none
none
振興 (promotion)

grey
none
none
壁 (wall)
電柱 (utility pole)
容疑 (suspicion)
うわさ (rumor)
底 (bottom)

Table 3: Words that were strongly associated with colors (without contexts)# of votes
11
10
98
7
65

white
雪 (snow)
下着 (underwear)
スキー (skiing)
蒸気 (steam)
個人 (individual)
メッセージ (message)
序盤 (early part)

blacknone
銃 (gun)点 (dot)ピアノ (piano)
重み (weight)
連敗 (losing streak)
インサイダー (insider)red
赤字 (deficit)
情熱 (passion)
火災 (fire)
重傷 (serious injury)
禁止 (prohibition)
本気 (earnestness)
侵害 (invasion)green
庭 (garden)
植物 (plant)
栽培 (cultivation)
ふるさと (hometown)
登山 (climbing)
農家 (farmer)
姿勢 (posture)# of votes
111098765

yellownone
金 (money)
ゴールデン (golden)
メダル (medal)
マネー (money)
料金 (charge)
画期的 (epoch-making)blue
海軍 (navy)
地球 (the earth)
冷静 (calmness)
流れ (flow)
数字 (figure)
請求 (request)
高校 (high school)

brown
none
土地 (land)イノシシ (wild boar)
レンガ (brick)士 (-er)
山岳 (mountains)
事情 (circumstances)

pink
none
姫 (princess)
交際 (romance)
カップル (couple)
娘 (daughter)
女優 (actress)
長女 (the oldest daughter)# of votes
111098765

purplenone
紫 (purple)
none
none
none
none
宗教 (religion)orange
none
none
none
none
none
意欲 (will)
家族 (family)

grey
新聞 (newspaper)
不透明 (not clear)none
廃刊 (ceased publication)
道路 (road)
像 (stature)
仕事 (job)2122

were statistically significantly different from the uniform
distribution was 743 among 2, 903 words (25.6%) whencontexts were considered. Conversely, this number was 908(31.3%) when contexts were not considered. This suggeststhat context information prevents word–color associations.
We used a chi-squared test for these analyses (p < 0.01).

4.2. Characteristics of Japanese Associations
We employed the same colors used in the study by Mohammad. The third row in Table 1 gives the percentage ofcolors that were associated with English words (Mohammad, 2011). He reported that the order of the most frequently associated colors was identical to the Berlin andKay order (i.e., white, black, red, ...). However, from thesecond row in Table 1, it is apparent that the order is different in Japanese (i.e., white, red, blue, ...). Although theseresults cannot be simply compared, this may demonstratethat word–color associations depend on language.
Tables 2 and 3 give examples of words that were strongly
associated with specific colors. For example, we can see
from Table 2 that 11 participants annotated white to 積雪(snow) with a context. As for Table 1, we randomly chose
one context for each word to count the number of votes for
Table 2. Additionally, we randomly chose one word foreach number of votes to construct the table. Because theresults in the tables fit our hypothesis, we considered thatthe word–color associations we collected to be reliable.
From the two tables, we can see that words were more concrete were more likely to be associated with the colors thatfrequently accompany the real entities to which they refer.
For example, 積雪 (snow) was associated with white, ピアノ (piano) was associated with black, トマト (tomato) was
associated with red, and so on. As can be seen from the firstand second rows in Table 1, minor colors were not strongly
associated with many words. For example, few words werefrequently associated with purple or orange. All of theseresults are available from our github.2
Further analyses revealed that in Japanese, words containing the same character tend to be associated with the samecolor. For example, “情熱” (passion), “熱心” (eagerness),and “熱湯” (hot water) contain the same character “熱”(heat), and these words were most frequently associatedwith the color red. The majority of color associations madefor the word “熱” (heat) were also red, indicating that inJapanese, color associations for a word represented by agroup of characters are related to those made for the individual characters.

both with and without contexts. Additionally, we plan to investigate the correlation between imagery and the strengthof color associations based on the associations collected inthis study.

Acknowledgments
This work was supported by JSPS KAKENHI Grant Number 26919018.

6. Bibliographical References
Berlin, B. and Kay, P. (1969). Basic Color Terms: TheirUniversality and Evolution. Berkeley: University ofCalifornia Press.
Kurohashi, S. and Nagao, M. (1998). Building a JapaneseParsed Corpus while Improving the Parsing System. In
Proceedings of the 1st International Conference on Language Resources and Evaluation (LREC 1998), pages
719–724.
Lafourcade, M., Brun, N. L., and Zampa, V. (2014).
Crowdsourcing Word-Color Associations. In Proceedings of the 19th International Conference on Application of Natural Language to Information Systems (NLDB2014), pages 39–44.
Mohammad, S. M. (2011). Even the Abstract have Colour:Consensus in Word-Colour Associations. In Proceedings of the 49th Annual Meeting of the Association for
Computational Linguistics (ACL 2011), pages 19–24.
Özbal, G., Strapparava, C., Mihalcea, R., and Pighin, D.
(2011). A Comparison of Unsupervised Methods to Associate Colors with Words. In Proceedings of the 4th BiAnnual International Conference on Affective Computing and Intelligent Interaction (ACII 2011), pages 42–51.
Volkova, S., Dolan, W. B., and Wilson, T. (2012). CLex:
A Lexicon for Exploring Color, Concept and Emotion
Associations in Language. In Proceedings of the 12th
Conference on the European Chapter of the Association
for Computational Linguistics (EACL 2012), pages 306–314.

5. Conclusion
As a result of this study, we constructed a novel language
resource for accessing word–color associations in Japanese,
which is available from our github. Adding to previousstudies, we focused on word–color associations in Japanesein consideration of the influence of contexts for each word.
We reported on the effects of context information and thecharacteristics of Japanese word–color associations.
In future work, we plan to develop a method for predicting
which colors are associated with specific Japanese words,
2

github.com/jun-harashima/word color association

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