Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Classification of Characters in Movie by Correlation Analysis of Genre and Linguistic Style

  • Received : 2018.11.29
  • Accepted : 2018.12.13
  • Published : 2019.01.31
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Abstract

The character dialogue created by AI is unnatural when compared with human-made dialogue, and it can not reveal the character's personality properly in spite of remarkable development of AI. The purpose of this paper is to classify characters through the linguistic style and to investigate the relation of the specific linguistic style with the personality. We analyzed the dialogues of 92 characters selected from total 60 movies categorized four movie genres, such as romantic comedy, action, comedy and horror/thriller, using Linguistic Inquiry and Word Count (LIWC), a text analysis software. As a result, we confirmed that there is a unique language style according to genre. Especially, we could find that the emotional tone than analytical thinking are two important features to classify. They were analyzed as very important features for classification as the precision and recall is over 78% for romantic comedy and action. However, the precision and recall were 66% and 50% for comedy and horror/thriller. Their impact on classification was less than romantic comedy and action genre. The characters of romantic comedy deal with the affection between men and women using a very high value of emotional tone than analytical thinking. The characters of action genre who need rational judgment to perform mission have much greater analytical thinking than emotional tone. Additionally, in the case of comedy and horror/thriller, we analyzed that they have many kinds of characters and that characters often change their personalities in the story.

Keywords

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Fig. 1. The dialogue of the character ‘Gigi’ in the script of Movie

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Fig. 2. The dialogue of the main character ‘Summer’ of the movie <500 Days of Summer>

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Fig. 3. Value of features for character dialogue

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Fig. 4. T-test results for 93 features

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Fig. 5. Scatter plot of ‘Tone’ and ‘Analytic’

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Fig. 6. KNN(k=9) classification for ‘Analytic’ and ‘Tone’

Table 1. Selected movie characters

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Table 2. LIWC features and words in each feature

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Table 3. Six hypotheses for four genres

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Table 4. Experimental result of classification

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