Mood Map visualizes the moods of Korean people in color and light through textual analysis of their Tweets on Twitter. We will create a custom software program in Processing that will search and analyze Tweets in Korean language through the Twitter API. Tweets will be analyzed using a text analysis library that searches for specific strings of Korean characters that describe certain moods or feelings.
There are 6 main categories of feelings or moods we will search and visualize: joy/pride, love, fear/ shame, anger, pity, and sadness/frustration. Mood Map cycles through 3 visualization sequences. The first sequence displays occurrences of tweets in real time. The second sequence is showing collective data of two moods in past one hour. And the third is collective data of one mood in a day.
This sequence control the intensity of color associated with each mood/feeling. The 6 mood/feeling categories will be associated with 6 fiber optic illuminators, each with a specific color. Each illuminator will be paired to two other illuminators through the connection of fiber optic cables. So as the intensity of certain moods changes over time, visitors can witness the relative expression of all the moods compared to each other, changing dynamically over time every 30 seconds. The overall composition will express a flux of mood, feeling, intensity and time transmitted to a spatial 3D body. Mood map is built and exhibited at “Data Curation” in Museum of Art at Seoul National University.
Visualization: Noa Younse
Assembly team: KwangYeon Cho, Kibum Park, Young-Won Chi, Byunghwa Kim, Min Jae Lee, Sang Ki Nam, Daram Park, Sooyoung Park, HyunWoo Yoo
Photography: E/B Office