Sonification can be defined as any technique that translates data into non-speech sound with a systematic, describable, and reproducible method, in order to reveal or facilitate communication, interpretation, or discovery of meaning that is latent in the data. This paper describes an approach for communicating cross-cultural differences in sentiment data through sonification, which is a powerful technique for the translation of patterns into sounds that are understandable, accessible, and musically pleasant. A machine-learning classifier was trained on sentiment information of two samples of Tweets from Singapore and New York with the keyword of "happiness." Positive-valence words that relate to the concept of happiness showed stronger influences on the classifier than negative words. For mapping, Tweet frequency differences of the semantic variable "anticipation" affected tempo, positive-affected pitch, and joy-affected loudness, while "trust" affected rhythmic regularity. The authors evaluated sonification of the original data from the two cities, together with a control condition generated from random mappings in a listening experiment. Results suggest that the original was rated as significantly more pleasant.
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