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Next, relationship information is extracted through their interrelationships and emotions are classified from the text expressed as relationship information. This allows us to analyze the themes and emotions of literary works more deeply. Emotion recognition is a key element of social interaction, and with the development of artificial intelligence technology, interaction between humans and machines is gradually expanding.
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The purpose of this study is to propose a visualization method based on sentiment analysis of text. For this purpose, sentiment analysis technology is used to analyze the emotions of the text in the dimensions of valence, arousal, and dominance. Accordingly, this study applied the prisma methodology to conduct a systematic literature review on Korean text-based sentiment analysis and identified 26 data sets and 19 models from a total of 19 papers.
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Therefore, among the fields of natural language processing, the field of emotion recognition focuses on human emotions. First, identify the objects (characters) that appear in the text story. As a case study, this study selects specific incidents in three occupational groups (professors, prosecutors, and doctors) that have become social issues and analyzes the public's perception of these incidents.
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Recent advances in AI technology have made natural language processing and emotional analysis possible. Accordingly, this study applied the prisma methodology to conduct a systematic literature review on Korean text-based sentiment analysis and identified 26 data sets and 19 models from a total of 19 papers. Kote is useful for everyday online text analysis.
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This paper deals with emotion analysis, which uses text data used on Twitter to classify users' emotional states into four types of emotions: joy, anger, sadness, and joy.
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