Abstract
Colors have a very important role on our perception of the world. We often associate colors with various concepts at different levels of consciousnes and these associations can be relevant to many fields such as education and advertisement. However, to the best of our knowledge, there are no systematic approaches to aid the automatic development of resources encoding this kind of knowledge. In this paper, we propose three computational methods based on image analysis, language models, and latent semantic analysis to automatically associate colors to words. We compare these methods against a gold standard obtained via crowd-sourcing. The results show that each method is effective in capturing different aspects of word-color associations.
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Özbal, G., Strapparava, C., Mihalcea, R., Pighin, D. (2011). A Comparison of Unsupervised Methods to Associate Colors with Words. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_5
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DOI: https://doi.org/10.1007/978-3-642-24571-8_5
Publisher Name: Springer, Berlin, Heidelberg
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