Classificação de filmes: uma abordagem utilizando o LIWC
Resumo
Esse artigo tem o objetivo de apresentar uma abordagem para classificação de filmes com base em suas legendas e informações extraídas de redes sociais. A metodologia desenvolvida utiliza o programa LIWC, que contém um dicionário de palavras que permite extrair características linguísticas, psicológicas e sociais de textos. Os resultados preliminares foram bastante satisfatórios, indicando direções promissoras para esse trabalho.
Referências
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Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10–18.
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Mullen, T. and Collier, N. (2004). Sentiment analysis using support vector machines with diverse information sources. In EMNLP, volume 4, pages 412–418.
Nascimento, P., Aguas, R., Lima, D., Kong, X., Osiek, B., Xexéo, G., and Souza, J. (2012). Análise de sentimento de tweets com foco em notícias. In Brazilian Workshop on Social Network Analysis and Mining.
Oliveira, E., Martins, P., and Chambel, T. (2011). Ifelt: Accessing movies through our emotions. In Proceddings of the 9th International Interactive Conference on Interactive Television, EuroITV ’11, pages 105–114, New York, NY, USA. ACM.
Pennebaker, J. W. and Seagal, J. D. (1999). Forming a story: The health benefits of narrative. Journal of clinical psychology, 55(10):1243–1254.
Picard, R. W. (1997). Affective Computing. MIT Press, Cambridge, MA, USA.
Poria, S., Cambria, E., Bajpai, R., and Hussain, A. (2017). A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 37:98–125.
Wortman, J. (2010). Film classification using subtitles and automatically generated language factors. Technion-Israel Institute of Technology, Faculty of Industrial and Management Engineering.
Ye, Q., Shi, W., and Li, Y. (2006). Sentiment classification for movie reviews in chinese by improved semantic oriented approach. In System Sciences, 2006. HICSS’06. Proceedings of the 39th Annual Hawaii International Conference on, volume 3, pages 53b–53b. IEEE.
Ashby, F. G., Valentin, V. V., et al. (2002). The effects of positive affect and arousal and working memory and executive attention: Neurobiology and computational models.
Bao, S., Xu, S., Zhang, L., Yan, R., Su, Z., Han, D., and Yu, Y. (2012). Mining social emotions from affective text. Knowledge and Data Engineering, IEEE Transactions on, 24(9):1658–1670.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10–18.
Han, J., Pei, J., and Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Jain, A. and Zongker, D. (1997). Feature selection: Evaluation, application, and small sample performance. IEEE transactions on pattern analysis and machine intelligence, 19(2):153–158.
Koch, R. (1999). The 80/20 Principle: The Secret of Achieving More with Less. A Currency book. Doubleday.
Marg, E. (1995). Descartes’error: Emotion, reason, and the human brain. Optometry & Vision Science, 72(11):847–848.
Mullen, T. and Collier, N. (2004). Sentiment analysis using support vector machines with diverse information sources. In EMNLP, volume 4, pages 412–418.
Nascimento, P., Aguas, R., Lima, D., Kong, X., Osiek, B., Xexéo, G., and Souza, J. (2012). Análise de sentimento de tweets com foco em notícias. In Brazilian Workshop on Social Network Analysis and Mining.
Oliveira, E., Martins, P., and Chambel, T. (2011). Ifelt: Accessing movies through our emotions. In Proceddings of the 9th International Interactive Conference on Interactive Television, EuroITV ’11, pages 105–114, New York, NY, USA. ACM.
Pennebaker, J. W. and Seagal, J. D. (1999). Forming a story: The health benefits of narrative. Journal of clinical psychology, 55(10):1243–1254.
Picard, R. W. (1997). Affective Computing. MIT Press, Cambridge, MA, USA.
Poria, S., Cambria, E., Bajpai, R., and Hussain, A. (2017). A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 37:98–125.
Wortman, J. (2010). Film classification using subtitles and automatically generated language factors. Technion-Israel Institute of Technology, Faculty of Industrial and Management Engineering.
Ye, Q., Shi, W., and Li, Y. (2006). Sentiment classification for movie reviews in chinese by improved semantic oriented approach. In System Sciences, 2006. HICSS’06. Proceedings of the 39th Annual Hawaii International Conference on, volume 3, pages 53b–53b. IEEE.
Publicado
02/07/2017
Como Citar
TAVARES, Rian; GUEDES, Gustavo Paiva.
Classificação de filmes: uma abordagem utilizando o LIWC. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 6. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 573-578.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2017.3247.
