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:
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
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.
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.
Post a Comment