Understanding human perceptual experience in unstructured data on the web

Computing for human experience has become more important for understanding all of aspects of any interaction of human beings in the cyber, physical, and social environments. In particular, artificial intelligent technologies based on big data enable to understand natural language, enhance day to day human experience, and make a better decision. In this paper, we propose a method to classify unstructured text data on the Web into the five types of sensation features: sight (ophthalmoception), hearing (audioception), touch (tactioception), smell (olfacception), and taste (gustaoception). Even though sensation is the first process of human experience against the environments, the study of sensation information extraction is neglected due to lack of sensory expression and knowledge comparing with the sentimental analysis or opinion mining. We first define the sensation measurement that is assigned to each feature. Then, we identify which sensation feature has a strong influence on human perceptual experience in a specific topic of corpus. Finally, we evaluate our method by comparing with several baselines in terms of the accuracy.