Abstract
TV series is one of the most popular entertainment media globally and is a representation of popular culture. It reflects a specific group's daily life culture and certain characteristics of society such as social norms. This paper improved and verified a new cross-cultural analysis method by analyzing facial expressions, original text features and audio features extracted from TV series datasets. We adopted the TV series from America, Japan, and Korea and extracted the textual features from the original text database rather than the translated one. We added the emotional frequency of text and part-of-speech frequency in the text modality. The emotional frequency of facial expressions and text were combined to explore the relation between nonverbal and verbal expressions. In addition, the feasibility of using audio features to further extend the new cross-cultural analysis method were explored. Overall, 1656 features extracted from 90 TV dramas were analyzed, including 42 facial features, 32 text features and 1582 audio features. The statistical results of the feature comparisons revealed the similarities and differences between the three countries and agreed with many existing theories, which resulted in traditional cross-cultural studies. Machine learning models of random forest and support vector machine were used for feature selection and classification to enhance the understanding of important features and conduct country classification.
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References
Tylor, E.B.: Primitive culture: researches into the development of mythology, philosophy, religion, art, and custom. J. Murray 1, 1 (1871)
Rohner, R.P.: Toward a conception of culture for cross-cultural psycholog. J. Cross-Cult. Psychol. 15(2), 111–138 (1984)
Ilesanmi, O.O.: What is cross-cultural research? Int. J. Psychol. Stud. 1(2), 82 (2009)
Black, J.S., Mendenhall, M.: Cross-cultural training effectiveness: a review and a theoretical framework for future research. Acad. Manag. Rev. 15(1), 113–136 (1990)
Hofstede, G.: Culture’s Consequences: International Differences in Work-Related Values. Sage, Beverly Hills (1980)
Chang, L.: Socialization and social adjustment of single children in China. Int. J. Psychol. 39, 390 (2004)
Li, H.Z.: Culture and gaze direction in conversation. RASK 20, 3–26 (2004)
Ji, L.J., Nisbett, R.E., Su, Y.J.: Culture, change and prediction. Psychol. Sci. 12, 450–456 (2001)
Sternberg, R.J.: Culture and intelligence. Am. Psychol. 59, 325–338 (2004)
Mundy-Castle, A.C.: Social and technological intelligence in Western and non-Western cultures. Universitas Univ. Ghana Legos 4, 42–45 (1974)
Smith, P.B., Bond, M.H., Kağitçibasi, C.: Understanding Social Psychology Across Cultures. SAGE Publications Ltd. (2006)
Treisman, A.: The effects of redundancy and familiarity on translating and repeating back a foreign and a native language. Br. J. Psychol. 56, 369–379 (1965)
Nida, E.: Toward a science of translation. E, J. Brill, Leiden, Netherlands (1964)
Miller, G.A., Beebe-Center, J.G.: Some psychological methods for evaluating the quality of translations. Mech. Transl. 3, 73–80 (1956)
Williams, R.: Keywords. Fontana, London (1983)
Storey, J.: Cultural Theory and Popular Culture: An Introduction, 8th edn., p. 2. Routledge (2018)
Weber, E., Ames, D., Blais, A.-R.: “How do i choose thee? Let me count the ways”: a textual analysis of similarities and differences in modes of decision-making in China and the United States. Manag. Organ. Rev. 1(01), 87–118 (2005)
Hatzithomas, L., Zotos, Y., Boutsouki, C.: Humor and cultural values in print advertising: a cross-cultural study. Int. Mark. Rev. 28(1), 57–80 (2011)
Xu, X.: A new cross-cultural research method based on multimodal features. [I], pp. 6–12. Tsinghua University, Beijing (2018)
Kashima, Y., Kashima, E.: Culture and language: the case of cultural dimensions and personal pronoun use. J. Cross-Cult. Psychol. 29, 461–468 (1998)
Semin, G.R., Gorts, C.A., Nandram, S., Semin-Goossens, A.: Cultural perspectives on the linguistic representation of emotion and emotion events. Cogn. Emot. 16, 11–28 (2002)
Bostanov, V., Kotchoubey, B.: Recognition of affective prosody: continuous wavelet measures of event-related brain potentials to emotional exclamations. Psychophysiology 41(2), 259–268 (2004)
Trompenaars, F., Hampden-Turner, C.: Riding the waves of culture: understanding diversity in global business. Nicholas Brealey International (2011)
Smee, A., Brennan, M., Hoek, J., Macpherson, T.: A test of procedures for collecting survey data using electronic mail. In: Refereed WIP Paper, Australian and NewZealand Marketing Academy (ANZMAC) Conference Proceedings, 30 November– 2 December, pp. 2447–2452. University of Otago, Dunedin, New Zealand (1998)
Comley, P.: The Use of the Internet as a Data Collection Method, Media Futures Report. Henley Centre, London (1996)
Tanzer, N., Sim, C.Q.E., Spielberger, C.D.: Experience and expression of anger in a Chinese society: the case of Singapore. In: Spielberger, C.D., Sarason, I.G., et al. (eds.) Stress and Emotion: Anxiety, Anger and Curiosity, vol. 16, pp. 51–65. Taylor & Francis, Washington, DC (1996)
Smith, P.B., Peterson, M.F., Schwartz, S.H., et al.: Cultural values, sources of guidance and their relevance to managerial behavior: A 47-nation study. J. Cross-Cult. Psychol. 33, 188–208 (2002)
Israel, J., Tajfel, H.: Context of Social Psychology: A Critical Assessment. Academic Press, London (1972)
Moscovici, S.: Society and theory in social psychology. In: Israel, J., Tajfel, H. (eds.) The Context of Social Psychology: A Critical Assessment, pp. 17–68. Academic Press, London (1972)
Matsumoto, D.: More evidence for the universality of a contempt expression. Motiv. Emot. 16, 363–368 (1992)
Ekman, P.: Universals and cultural differences in facial expressions of emotion. In: Cole, J. (ed.) Nebraska Symposium on Motivation, vol. 19, pp. 207–282. University of Nebraska Press, Lincoln (1972)
Ekman, P., Friesen, W.V., O'Sullivan, M., et al.: Universals and cultural differences in the judgment of facial expressions of emotion. J. Pers. Soc. Psychol. S3, 712-71 (1987)
Koopmann-Holm, B., Matsumoto, D.: Values and display rules for specific Emotions. J. Cross-Cult. Psychol. 42(3), 355–371 (2011)
Matsumoto, D., Kupperbusch, C.: Idiocentric and allocentric differences in emotional expression, experience and the coherence between expression and experience. Asian J. Soc. Psychol. 4, 113–131 (2001)
Agnew, C., Van Lange, P., Rusbult, C., Langston, C., Insko, C.A.: Cognitive interdependence: Commitment and the mental representation of close relationships. J. Pers. Soc. Psychol. 74, 939–954 (1998)
Fitzsimons, G.M., Kay, A.C.: Language and interpersonal cognition: causal effects of variations in pronoun usage on perceptions of closeness. Pers. Soc. Psychol. Bull. 30(5), 547–557 (2004)
Scherer, K.R., Banse, R., Wallbott, H.G.: Emotion inferences from vocal expression correlate across languages and cultures. J. Cross-Cult. Psychol. 32(1), 76–92 (2001)
Thompson, W.F., Balkwill, L.L.: Decoding speech prosody in five languages. Semiotica 158, 407–424 (2006)
Besson, M., Magne, C., Schön, D.: Emotional prosody: sex differences in sensitivity to speech melody. Trends Cogn. Sci. 6(10), 405–407 (2002)
Poriaa, S., Cambriac, E., Bajpaib, R., Hussaina, A.: A review of affective computing: from unimodal analysis to multimodal fusion. Inf. Fusion 37, 98–125 (2017)
Xu, C., Cetintas, S., Lee, K., Li, L.: Visual Sentiment Prediction with Deep Convolutional Neural Networks (2014)
Poria, S., Chaturvedi, I., Cambria, E., Hussain, A.: Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: Proceedings of ICDM, Barcelona (2016)
Mishne, G., et al.: Experiments with mood classification in blog posts. In: Proceedings of ACM SIGIR 2005 Workshop on Stylistic Analysis of Text for Information Access 19, pp. 321–327. Citeseer (2005)
Eyben, F., Wollmer, M., Schuller, B.: Openear—introducing the Munich open-source emotion and affect recognition toolkit. In: 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1–6. IEEE (2009)
Anand, N., Verma, P.: Convoluted feelings convolutional and recurrent nets for detecting emotion from audio data. Technical report, Stanford University (2015)
Pérez-Rosas, V., Mihalcea, R., Morency, L.: Utterance-level multimodal sentiment analysis. In: ACL, no. 1, pp. 973–982 (2013)
Poria, S., Cambria, E., Gelbukh, A.: Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis. In: Proceedings of EMNLP, pp. 2539–2544 (2015)
Morency, L., Mihalcea, R., Doshi, P.: Towards multimodal sentiment analysis: harvesting opinions from the web. In: Proceedings of the 13th International Conference on Multimodal Interfaces, pp. 169–176. ACM (2011)
Poria, S., Cambria, E., Howard, N., Huang, G.B., Hussain, A.: Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174, 50–59 (2016)
Weninger, F., Knaup, T., Schuller, B., Sun, C., Wollmer, M., Sagae, K.: Youtube movie reviews: sentiment analysis in an audio-visual context. Intell. Syst. IEEE 28(3), 46–53 (2013)
Eyben, F, Wollmer, M., Schuller, B.: OpenSMILE-The Munich versatile and fast open-source audio feature extractor. In: Proceedings of ACM Multimedia (MM), Florence, Italy, pp. 1459–1462 (2010)
Cambria, E., Poria, S., Hazarika, D., Kwok, K.: SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: AAAI, pp. 1795–1802 (2018)
Chen, S., Hsu, C., Kuo, C., Ting-Hao, T., Huang, H., Ku, L.: Emotionlines: an emotion corpus of multi-party conversations (2018). arXiv preprint arXiv:1802.08379
Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., Mihalcea, R.: MELD: a multimodal multi-party dataset for emotion recognition in conversation. In: ACL (2019)
Bird, S., Loper, E., Klein, E.: Natural Language Processing with Python. O’Reilly Media Inc. (2009)
Kudo, T.: Mecab: yet another part-of-speech and morphological analyzer (2006). https://mecab.sourceforge.net
Kitayama, S., Markus, H.R., Kurokawa, M.: Culture, emotion, and well-being: good feelings in japan and the United States. Cogn. Emot. 14(1), 93–124 (2000)
Markus, H.R., Kitayama, S.: Culture and the self: implications for cognition, emotion, and motivation. Psychol. Rev. 98(2), 224 (1991)
Fiske, A.P., Kitayama, S., Markus, H.R., Nisbett, R.E.: The cultural matrix of social psychology. In: Gilbert, D.T., Fiske, S.T., Lindzey, G. (eds.) The Handbook of Social Psychology, vol. 2, 4th edn., pp. 915–981. McGraw Hill, New York (1998)
Park, H.S., Levine, T.R.: The theory of reasoned action and selfconstrual: evidence from three cultures. Commun. Monogr. 66(3), 199–218 (1999)
Singelis, T.M., Sharkey, W.F.: Culture, self-construal, and embarrassability. J. Cross-Cult. Psychol. 26(6), 622–644 (1995)
Hofstede, G.: Culture’s Consequences: COMPARING values, Behaviours, Institutions and Organizations Across Nations, 2nd edn. Sage, Thousand Oaks (2001)
Hall, E.T.: Beyond Culture. Anchor Books/Doubleday, Garden City (1976)
Abramson, P.R., Pinkerton, S.D.: Sexual Nature/Sexual Culture. [S.l.]: University of Chicago Press (1995)
Soh, C.S.: The Comfort Women: Sexual Violence and Postcolonial Memory in Korea and Japan. [S.l.]: University of Chicago Press (2008)
Robertson, J.: Takarazuka: Sexual Politics and Popular Culture in Modern Japan. [S.l.]: University of California Press (1998)
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Lai, X., Qie, N., Rau, PL.P. (2021). Cultural Differences Demonstrated by TV Series: A Cross-Cultural Analysis of Multimodal Features. In: Rau, PL.P. (eds) Cross-Cultural Design. Experience and Product Design Across Cultures. HCII 2021. Lecture Notes in Computer Science(), vol 12771. Springer, Cham. https://doi.org/10.1007/978-3-030-77074-7_34
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