The Emotions & Colors In-Between

Updated: May 30, 2019



For the final assignment of Reading & Writing Electronic Text, I attempted to use Python to find and match nuanced emotions with various colors.


Inspiration


I drew from Plutchik's Wheel of Emotions that identifies eight basic emotions with varying levels of intensity:


Joy

Trust

Fear

Surprise

Sadness

Disgust

Anger

Anticipation


Each of these basic emotions are matched with a color on the wheel.

Plutchik's Wheel of Emotions

I was also further inspired by Alan Watkins Ted talk about how we possess 34,000 emotions. This led me to wonder if it was possible to find the emotions in-between each other on Plutchik's wheel. While the eight basic emotions were clearly laid out, I felt there were so many more than these basic emotions and aimed to seek them out. Emotions are complicated and nuanced and we often have a hard time describing exactly how we feel. How many different emotions do we feel everyday and where do they fall on this wheel?


I was also interested in linking these emotions to an associated color. Emotions are universal in all of us; we all feel them. However, colors associated with different emotions can differ between certain cultures. Is it possible to universally associate an emotion with a specific color? If it isn't, what are the emotional responses and reactions we have when we associate a color we don't feel fits the emotion?


The aim of this project was to attempt to navigate the nuances of different emotions from Plutchick's wheel as well as the different in-between colors he associated with each of these emotions.



Process


First, I translated Plutchik's wheel of emotion into a source text to use, which left me with this:

Trust goes from acceptance to admiration

Fear goes from apprehension to terror

Surprise goes from distraction to amazement

Sadness goes from pensiveness to grief

Disgust goes from boredom to loathing

Anger goes from annoyance to rage

Anticipation goes from interest to vigilance

Joy goes from serenity to ecstasy


Once I had a source text to work with, I wanted to use word vectors to find similar in meaning words to these emotions. I used Simple Neighbors to look up and choose a random nearest word vector for each of these emotions, giving me similar, but different emotional outputs. Simple neighbors returns the closest items for any given neighbor (using nearest neighbor search, a technique for finding similar points in high-dimensional space).


From these new and similar generated emotional outputs, I took the last emotion from the original source and the generate source:


Using these words, I used vector arithmetic to find the closest words to the halfway point between the original word and the new word. For example, admiration and reciprocated:


With this list of words in-between admiration and reciprocated, I then used the associated color to the original word from Plutchik's color wheel and matched each of the emotions to a different type of that color. In this case the color was light green:


I repeated this process for each of the eight basic emotions on the wheel, giving me a final output like this:



Trust goes from acceptance to admiration

reciprocated is light green

companionship is easter green

admiration is spring green

affectionate is light grass green

fondness is key lime

affection is light lime

kindness is light green

friendliness is pale lime green

benevolence is baby green

sympathy is mint green

unconditional is pale lime Trust turns from expectation to reciprocated


Fear goes from apprehension to terror

fear is green

fright is kelly green

dread is irish green

spectre is true green

dreads is emerald green

horrors is kelley green

unspeakable is grass green

scary is vibrant green

frightening is grassy green

terrible is emerald

nightmares is shamrock

terrors is tree green Fear goes from suspecting to mastermind


Surprise goes from distraction to amazement

amazement is dark green

awe is very dark green

disbelief is dark forest green

incredulity is hunter green

dismay is racing green

disgust is dark green

displeasure is bottle green

pity is forest green

fright is british racing green

dread is pine green Surprise goes from diversion to awe


Sadness goes from pensiveness to grief

grieving is blue

mourning is electric blue

grief is azul

sorrows is blue blue

heartache is vivid blue

sadness is bright blue

sorrow is cerulean blue

melancholy is rich blue

heartbreak is true blue

lament is deep sky blue

gloom is sapphire Sadness goes from heartache to mourning


Disgust goes from boredom to loathing

dismay is purple

disgust is warm purple

displeasure is darkish purple

loathing is light eggplant

disapproval is purply

scorn is medium purple

disdain is ugly purple

distaste is barney purple

hatred is bruise purple

fester is purple/blue

resentment is bluey purple Displeasure turns from rut to dismay


Anger goes from annoyance to rage

ferocity is red

seething is fire engine red

fury is bright red

unleashed is tomato red

pent is cherry red

rage is scarlet

anger is vermillion

hatred is orangish red

outcry is cherry

indignation is lipstick red

outrage is darkish red

uproar is neon red

Resentment turns from embarrassment to ferocity

Anticipation goes from interest to vigilance

scrutinized is orange

vigilance is pumpkin orange

scrutiny is pumpkin

vetting is bright orange

procedural is tangerine

justices is blood orange

jurisprudence is browny orange

judiciary is deep orange

judicial is reddish orange

inquiry is dirty orange

concerns is rusty orange Agitated goes from interest to scrutiny


Joy goes from serenity to ecstasy

euphoria is yellow

ecstasy is bright yellow

cathartic is dandelion

intoxicating is sunshine yellow

comedown is lemon yellow

hyperactive is sunflower yellow

spastic is canary yellow

manic is off yellow

addictive is neon yellow

Joy turns from bliss to euphoria


I decided to sandwich the original text on top, with the list of the words (emotions) in between and the generated text coming after. I thought this was appropriate because this process took the reader on a journey through the different, but similar emotional states with different states of the same colors. Whether or not the reader agreed with these emotions being linked with the associated color, the aim was to elicit some kind of image, response or emotion through these outputs.


For the performance, I decided to show each of these generated colors with the text of the emotion in the middle:



Reading Performance


Reading the computer generated poem out loud at NYU Tisch.