Abstract
Social media gives new opportunities in customer survey and market survey for design inspiration with comments posted online by users spontaneously, in an oral-near language, and almost free of biases. This new source however has huge size and complexity of data needed to be processed. In this paper, we propose an automated way for processing these comments, using sentiment rating algorithm. Traps like negations, irony, smileys are considered in our algorithm. We validate it on the example of a commercial home theatre system, comparing our automated sentiment predictions with the one of a group of 15 test subjects, resulting in a satisfactory correlation.
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Raghupathi, D., Yannou, B., Farel, R., Poirson, E. (2015). Learning from Product Users, a Sentiment Rating Algorithm. In: Gero, J., Hanna, S. (eds) Design Computing and Cognition '14. Springer, Cham. https://doi.org/10.1007/978-3-319-14956-1_27
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DOI: https://doi.org/10.1007/978-3-319-14956-1_27
Publisher Name: Springer, Cham
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