Towards Building a Human Perception Knowledge for Social Sensation Analysis

Abstract

With the development of social network services, various phenomena can be shared easily and rapidly through human natural language, including not only natural, but also social-cultural phenomena. Consequently, analyses of social media have appreciated in value for understanding human behaviors to grasp public interests or sentiments, as both the medium and outcome of human experiences. From the state of the art psychology and neuroscience, human behaviors, regarding both physical and linguistic aspects, are mostly dependent on sensory perceptions under the realm of the subconscious. Even though sensation is the most fundamental element to understand human behaviors, the rack of background resources make it hard to study the social sensation comparing with the sentimental or opinion mining. This paper focuses on building sensation knowledges to obtain useful human perceptual experiences in natural language expressions, as a requisite for the social sensation analysis. We try to approach the constructing lexicons as a sensation knowledge from two viewpoints, such as a deep learning and lexicon based methods. Then we classify social media text based on the lexicons with considering a part of speech as well as semantic meanings of each word. Finally, we identify which knowledge has a good performance to distinguish sensation expressions from social media data in terms of accuracy and and F-score.