The Study of Character under the Application of Artificial Intelligence

-- Takes Ibn as an Example in "Desire under the Elm Tree"

Abstract

Character analysis in drama is a core issue in drama research, performance teaching and drama creation. However, traditional character analysis methods mainly rely on textual close reading, dramatic criticism, and actors' practical experience, and lack objective and systematic quantitative analysis means. With the rapid development of Artificial Intelligence (AI) technology, especially the maturity of Natural Language Processing (NLP) and Emotion Computing technology, the use of AI means to analyze the character of drama is gradually becoming an emerging method in the field of drama research. Character classification is an important element in theater research, psychology and AI text analysis. Different disciplines have different approaches to character classification, and in theater research, character classification helps to understand character motivation, action logic, and their performance in dramatic situations. In AI analysis, character classification is usually done with the help of Natural Language Processing (NLP) and machine learning techniques to extract features from play texts and classify them. In this paper, through the interdisciplinary combination of artificial intelligence technology and dramatic character analysis, taking Ibn in Desire Under the Elm Tree as an example, we discuss how to utilize methods such as Natural Language Processing (NLP), Sentiment Analysis (Sentiment Analysis), and Social Network Analysis (SNA), to carry out the study of dramatic character from multiple perspectives such as textual features, emotional tendencies, and character relationships. Systematic research. Combined with traditional theories of drama, such as Stanislavski's Action Analysis, Freud's psychological theories, and modern personality classification models (Big Five Personality, MBTI, etc.), this study demonstrates the potential value of AI technology in drama research.