Smart cities use various deployed sensors and aggregate their data to create a big picture of the live state of the city. This live state can be enhanced by incorporating the affective states of the citizens. In this work, we automatically detect the emotions of the city's inhabitants from geo-tagged posts on the social network Twitter. Emotions are represented as four-dimensional vectors of pleasantness, arousal, dominance and unpredictability. In a training phase, emotionword hashtags in the messages are used as the ground truth emotion contained in a message. A neural network is trained by using the presence of words, hashtags and emoticons in the messages as features. During the live phase, these features are extracted from new geo-tagged Twitter messages and given as input to the neural network. This allows the estimation of a four-dimensional emotion vector for a new message. The detected emotions are aggregated over space and time and visualized on a map of the city.
Other recent work assigned four dimensions, based on the Fontaine model (pleasantness, arousal, dominance and unpredictability), to 24 emotion hashtags and created a dataset with 58,000 tweets REF .
Detection and Visualization of Emotions in an Affect-Aware City
EMASC '14
Psychology Computer Science