Looking at this paper today:
The Emotional and Chromatic Layers of Urban Smells.
Daniele Quercia, Luca Maria Aiello, Rossano Schifanella. 2016.
The chart above shows the relation between a
smell-producing location, and the pleasantness of the words used on social
media near that place. Most positive words are used near the Food category, and
less near Waste. Nature has less happy words with it than Food, perhaps because
of the happy activities people are doing in these areas, i.e., eating and being
with friends. Regardless, this chart seems to make sense, and can serve as
proof that this odor-hedonics data can be predictive of people's emotions.
Who cares? Someone like an urban planner can use this to
get an idea of how to better arrange municipal facilities. Someone who wants to
update local land use regulations could use this data to see which areas of
town work and which ones don't. The study-authors mention the matching of this
data to 'most optimal route' data to give the 'most pleasurable route' through a city.
They used a lot of semantic-hedonic smell knowledge from
a previous study (Henshaw 2013) which organizes two lists of pleasant and
unpleasant smells, and I'd like to just copy it here:
Pleasant smells
bread, baked, baked goods, coffee, coffees, aftershave,
cut grass, grass, grassy, floral, flower, flowers, flowershop, flowery,
lavender, lilies, lily, magnolia, rose, rosey, tulip, tulips, violet, violets,
baby, babies, child, children, sea, seaside, countryside, cedar, cedarwood,
conifer, dry grass, earth, earthy, eucalyptus, ground, leafy, leaves, old wood,
pine, sandalwood, soil, tree, trees, wood, woodlands, woody, petrol, diesel,
fuel, gasoline, soap powder, soap
Unpleasant smells
flatulence, fart, vomit, dog shit, dogshit, excrement,
faeces, farts, feces, manure, shit, cigarette smoke, cigarette, cigarettes,
cigar, cigars, smoker, tabacco, tobacco, pee, piss, ammonia, urine, public
toilet, public toilets, toilet, toilets, urinal, urinals, gone-off milk, fish,
rotten fish, rotten food, rotten, rotten fruit, rotten fruits, putrid, bus,
buses, car, cars, exhaust, traffic, fume, fumes, body odour, body odor, sweat,
sweaty, dirty clothes
Henshaw, V. 2013. Urban Smellscapes:
Understanding and Designing City Smell Environments. Routledge.
Post Script:
This is how they got to the bottom of their smellscape
emotion chart:
"We set out to study the relationship between the
smellscape and emotions on our data. To do so, we need to have a lexicon of
emotion words. We use two of them: the “Linguistic Inquiry Word Count” (LIWC)
(Pennebaker 2013), that classifies words into positive and negative emotions,
and the “EmoLex” word-emotion lexicon (Mohammad and Turney 2013), that
classifies words into eight primary emotions based on Plutchik’s
psycho-evolutionary theory (Plutchik 1991) (i.e., anger, fear, anticipation,
trust, surprise, sadness, joy, and disgust).
Pennebaker, J. 2013. The Secret Life of Pronouns: What
Our Words Say About Us. Bloomsbury.
Plutchik, R. 1991. The emotions. University
Press of America.
Mohammad, S. M., and Turney, P. D. 2013.
Crowdsourcing a word–emotion association lexicon. Computational Intelligence
29(3):436–465.
