빅데이터의 감성가치 도출을 위한 기호학적 분석법 연구

This study attempted to overcome the limitations of the non-contextuality used in existing big data mining analysis, and to propose a semiotic mining analysis methodology that reflects the context between hidden information. This study deviates from the limitations of the existing data analysis methodology and applies a semiotic analysis method that can grasp the isotopes of the surface text, that is, the profound structure of meaning, along with the linguistic research methodology. This study tried to conceive a derivation process to grasp the emotional value that exists. Therefore, this study was able to conceive of an emotional value analysis process that can comprehensively explain not only the meaning of signs but also the meaning of sociocultural phenomena, and a subjective information analysis process motivated by the method of extracting the meaning of the text based on morpheme analysis and syntax analysis. In other words, in order to explore the new possibility of the big data semiotic analysis technique, a semiotic analysis process was envisioned in three stages: 1) morpheme analysis process, 2) isotope analysis process, and 3) emotional value analysis process. The analysis methodology extracts the subjective intention hidden in the text and the qualities of the emotional value for various things such as people, products, companies, etc., which are proper nouns entered in the analysis of emotional value, in fields such as marketing and research. This analysis methodology is expected to provide improved analysis tools that can be used in various ways.