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題名 以資料探勘探討日本戲劇收視率影響要素與其文化內涵
Exploring the Factors Influencing the Rating of Japanese Dramas and Its Cultural Connotation by Data Mining
作者 徐子心
Syu, Zih-Sin
貢獻者 羅崇銘
Lo, Chung-Ming
徐子心
Syu, Zih-Sin
關鍵詞 收視率預測
戲劇
日本
機器學習
深度學習
電視劇海報
ratings prediction
drama
Japan
machine learning
deep learning
drama poster
日期 2022
上傳時間 2-Sep-2022 14:58:32 (UTC+8)
摘要 戲劇透過與社會現象或觀念的同步，讓觀眾對情節或角色產生共鳴，驅使人們產生觀看下一集的慾望，而反映人們收看熱度指標的收視率更是決定廣告收益與後續周邊經濟效益的參考標準。在文化、電視劇、收視率三者關係密切的情況下，本研究利用自2003年至2020年間800部日本黃金時段之電視劇，使用屬性特徵的年度、季度、電視台、星期、時間點、類型、編劇、原作、續集、演員的共10個特徵進行預測外，更加入海報中的人臉特徵以判別海報中人臉資訊對於收視率預測的重要性。比較簡易貝氏、類神經網路、支援向量機、隨機森林的4種分類器之預測結果後，加入人臉特徵的隨機森林模型之準確率由75.80%增加至77.10%，說明了人臉資訊對於收視率的整體預測有所貢獻。另一方面本研究也利用卷積神經網路模型，得知單獨使用海報影像時預測電視劇收視率之準確率為71.70%，說明了在卷積神經網路上使用海報影像預測電視劇收視率的可用性，並自研究結果探討影響收視率的因素以及反映這些因素的整體國家之文化內涵。
Drama through the synchronization with social phenomena or way of thinking, allows the audience to resonate with the plot or characters, and lets people to have the desire to watch the next episode. And the ratings of people’s viewing indicators, which can be the standard of advertising revenue and subsequent economic efficiency of surrounding areas. According to our research the relativity between culture, TV dramas and ratings is very high, in this study we use broadcast year, broadcast season, TV stations, day of the week, broadcast season, genre, screenwriters, original work, sequel, actor and face detection features of 800 Japanese TV dramas broadcasting during prime time to predict the ratings. After using four classifiers: Naïve Bayes, artificial neural network, support vector machine, and random forest, the accuracy of the random forest model with face detection features increased from 75.80% to 77.10%, which proves face information can improve the accuracy of the overall prediction ratings. On the other side, we use drama posters to predict ratings based on convolutional neural network, the accuracy is 71.70%, proves that the availability of using poster to predict ratings with the convolutional neural network. The experimental show the factors that affect ratings and the cultural connotation of the country that reflects these factors.
參考文獻 日文文獻
TBSホールディングス(2022)。TBSテレビ。2022年1月3日，取自: https://www.tbs.co.jp/
TVer INC. (2022)。もっと、今をつなぐテレビへ。NOW ON TVer。2022年8月2日，取自: https://tver.jp/_s/campaign/nowontver/index.html
Video Research Ltd. (2021)。視聴率。2021年11月22 日，取自: https://www.videor.co.jp/service/media-data/tvrating.html
Nippon Television Network Corporation (2021)。日テレ広告ガイド。2021年11月25日，取自: https://ad.ntv.co.jp/guide/tvcm/index-spot2.html
ブリタニカジャパン株式会社 (2009)。ブリタニカ国際大百科事典:小項目版。東京:ロゴヴィスタ。
小学館 (1988)。日本大百科全書。東京:小学館。
中原美絵子(2014年3月14日)。日テレが、Hulu買収で仕掛ける「動画革命」。東洋経済。2021年10月30日，取自: https://toyokeizai.net/articles/-/32911
内閣府地方創生推進事務局 (2019)。まち・ひと・しごと創生長期ビジョン（令和元年改訂版）。2021年12月16日，取自: https://www.chisou.go.jp/sousei/info/pdf/r1-12-20-vision.pdf
内閣府景気統計部 (2021)。消費動向調査。2021年9月6日，取自: https://www.e-stat.go.jp/stat-search/file-download?statInfId=000032076871&fileKind=0
太田静 (2000)。私がみてる分, カウントされてますか?視聴率の調査方法。映像情報メディア学会誌，54(9)，1267-1268。doi:10.3169/itej.54.1267
日本国語大辞典第二版編集委員会、小学館国語辞典編集部、北原保雄 (2000)。日本国語大辞典 (第2版)。東京:小学館。
日本經濟新聞(2013年8月10日)。「あまちゃん」効果は32億円　岩手のシンクタンクが試算。日本經濟新聞。2021年10月24日，取自: https://www.nikkei.com/article/DGXNASDG1002W_Q3A810C1CR8000/
木村隆志 (2017年7月30日)。夏場のテレビ番組が迷走する理由 視聴習慣が乱れやすく制作側も迷い。ライブドアニュース。2021年6月23日，取自: https://news.livedoor.com/article/detail/13405190/
木村隆志 (2020年1月17日)。テレビドラマ｢刑事･医療系が75％｣の危険水域。東洋経済オンライン。2021年4月10日，取自: https://toyokeizai.net/articles/-/325250
北浦寛之 (2018)。テレビ成長期の日本映画:メディア間交渉のなかのドラマ。名古屋:名古屋大学出版会。
矢本成恒 (2008)。テレビ番組制作におけるエンジニアリング・ブランド。開発工学，28，27-30。doi:10.11363/kaihatsukogaku1984.28.27
佐藤裕 (2020年7月26日)。日曜劇場『半沢直樹』がコロナ禍の就活を変えるワケ。Yahoo!ニュース。2022年1月3日，取自: https://news.yahoo.co.jp/byline/yusato/20200726-00189950/
国土交通省総合政策局観光地域振興課、経済産業省商務情報政策局文化情報関連産業課、文化庁文化部芸術文化課(2005)。映像等コンテンツの制作・活用による地域振興のあり方に関する調査。2021年10月9日， 取自: http://www.mlit.go.jp/kokudokeikaku/souhatu/h16seika/12eizou/12eizou.htm
国立社会保障・人口問題研究所 (2017)。日本の将来推計人口。2021年11月1日，取自: http://www.ipss.go.jp/pp-zenkoku/j/zenkoku2017/pp29_gaiyou.pdf
香山リカ (2014年1月18日)。医療ものドラマはなぜウケるのか？。imidas。2022年6月26日，https://imidas.jp/josiki/?article_id=l-58-181-14-01-g320
產經新聞 (2017年2月23日)。「真田丸」の経済波及効果、長野では２００億９０００万円。產經新聞。2021年10月24日，取自: https://www.sankei.com/article/20170223-5G7Z5FEEG5JXFIHXDERWATDJ4A/
鳥山拡 (1993)。テレビドラマ⋅映画の世界(初版)。東京:早稲田大学出版社。
森晋也 (2020年10月24日)。4年間で164集落が消滅、人口減・高齢化で拍車。日本經濟新聞。2021年11月5日，取自: https://www.nikkei.com/article/DGXMZO65367920T21C20A0ML8000/
境治 (2017年12月27日)。世帯から個人へ､タイムシフトも反映。2018年､視聴率が変わる！。Yahoo!ニュース。2022年6月23日，取自: https://news.yahoo.co.jp/byline/sakaiosamu/20171227-00079793
福島悠介、山崎俊彦、相澤清晴 (2016)。放送前の情報のみを用いたテレビドラマの視聴率予測。映像情報メディア学会誌，70(11)，J255-J261。 doi:10.3169/itej.70.J255
総務省 (2019)。人口推計。2021年11月22日，取自: https://www.stat.go.jp/data/jinsui/2019np/pdf/2019np.pdf
総務省 (2020)。過疎地域等における集落の状況に関する現況把握調査報告書。2021年12月14日，取自: https://www.soumu.go.jp/main_content/000678497.pdf
総務省 (2021)。高齢者の人口。2021年11月1日，取自: https://www.stat.go.jp/data/topics/topi1291.html
総務省統計局 (2021)。令和3年労働力調査結果。2021年12月13日，取自:https://www.stat.go.jp/data/roudou/sokuhou/4hanki/dt/index.html
影山貴彦 (2019)。テレビドラマでわかる平成社会風俗史。東京 : 実業之日本社。
鎌田とし子、鎌田哲宏(2015)。「限界集落」における労働力の状態。日本労働社会学会年報，26，101-122. doi:10.20750/arls.arls026.101

英文文獻
Aboud, K. (2012). Medical dramas—the pros and the cons. Dermatology Practical & Conceptual, 2. doi:10.5826/dpc.0201a14
Adankon, M. M., & Cheriet, M. (2009). Support Vector Machine. In S. Z. Li & A. Jain (Eds.), Encyclopedia of Biometrics (pp. 1303-1308). Boston, MA: Springer US.
Agarwal, A., Das, R. R., & Das, A. (2021, 7-8 Oct. 2021). Machine Learning Techniques for Automated Movie Genre Classification Tool. Paper presented at the 2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE).
Agnes, M., & Guralnik, D. B. (2001). Webster`s New World college dictionary / Michael Agnes, editor in chief ; David B. Guralnik (4th ed.). New York: IDG Books Worldwide.
Ahn, J., Ma, K., Lee, O., & Sura, S. (2017). Do big data support TV viewing rate forecasting? A case study of a Korean TV drama. Information Systems Frontiers, 19(2), 411-420. doi:10.1007/s10796-016-9659-5
Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, 21-23 Aug. 2017). Understanding of a convolutional neural network. Paper presented at the 2017 International Conference on Engineering and Technology (ICET).
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., . . . Asari, V. K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164.
Araújo Vila, N., Fraiz Brea, J. A., & de Carlos, P. (2021). Film tourism in Spain: Destination awareness and visit motivation as determinants to visit places seen in TV series. European Research on Management and Business Economics, 27(1), 100135. doi:10.1016/j.iedeen.2020.100135
Arai, A., & Terano, T. (2005). Yutori Is Considered Harmful: Agent-Based Analysis for Education Policy in Japan. In R. Shiratori, K. Arai, & F. Kato (Eds.), Gaming, Simulations, and Society: Research Scope and Perspective (pp. 129-136). Tokyo: Springer Tokyo.
Biau, G., & Scornet, E. (2016). A random forest guided tour. TEST, 25(2), 197-227. doi:10.1007/s11749-016-0481-7
Bisong, E. (2019a). Ensemble Methods. In E. Bisong (Ed.), Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners (pp. 269-286). Berkeley, CA: Apress.
Bisong, E. (2019b). Introduction to Scikit-learn. In E. Bisong (Ed.), Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners (pp. 215-229). Berkeley, CA: Apress.
Bisong, E. (2019c). Support Vector Machines. In E. Bisong (Ed.), Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners (pp. 255-268). Berkeley, CA: Apress.
Boursier, V., Musetti, A., Gioia, F., Flayelle, M., Billieux, J., & Schimmenti, A. (2021). Is Watching TV Series an Adaptive Coping Strategy During the COVID-19 Pandemic? Insights From an Italian Community Sample. Frontiers in Psychiatry, 12(554). doi:10.3389/fpsyt.2021.599859
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. doi:10.1023/A:1010933404324
Bristi, W. R., Zaman, Z., & Sultana, N. (2019, 6-8 July 2019). Predicting IMDb Rating of Movies by Machine Learning Techniques. Paper presented at the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT).
Calzo, J. P., & Ward, L. M. (2009). Media Exposure and Viewers` Attitudes Toward Homosexuality: Evidence for Mainstreaming or Resonance? Journal of Broadcasting & Electronic Media, 53(2), 280-299. doi:10.1080/08838150902908049
Chang, B.-H., & Ki, E.-J. (2005). Devising a Practical Model for Predicting Theatrical Movie Success: Focusing on the Experience Good Property. Journal of Media Economics, 18(4), 247-269. doi:10.1207/s15327736me1804_2
Collins. (2021). prime time. Retrieved from https://www.collinsdictionary.com/dictionary/english/prime-time
Cross-Validation. (2009). In S. Z. Li & A. Jain (Eds.), Encyclopedia of Biometrics (pp. 206-206). Boston, MA: Springer US.
da Silva, I. N., Hernane Spatti, D., Andrade Flauzino, R., Liboni, L. H. B., & dos Reis Alves, S. F. (2017). Artificial Neural Network Architectures and Training Processes. In I. N. da Silva, D. Hernane Spatti, R. Andrade Flauzino, L. H. B. Liboni, & S. F. dos Reis Alves (Eds.), Artificial Neural Networks : A Practical Course (pp. 21-28). Cham: Springer International Publishing.
Danaher, P., & Dagger, T. (2012). Using a nested logit model to forecast television ratings. International Journal of Forecasting, 28(3), 607-622. doi:10.1016/j.ijforecast.2012.02.008
Dissanayake, W. (2012). Asian television dramas and Asian theories of communication. Journal of Multicultural Discourses, 7(2), 191-196. doi:10.1080/17447143.2012.666246
Fürnkranz, J. (2010). Decision Tree. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning (pp. 263-267). Boston, MA: Springer US.
Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). Knowledge Discovery and Data Mining: Towards a Unifying Framework.
Ghosh, A., Sufian, A., Sultana, F., Chakrabarti, A., & De, D. (2020). Fundamental Concepts of Convolutional Neural Network. In V. E. Balas, R. Kumar, & R. Srivastava (Eds.), Recent Trends and Advances in Artificial Intelligence and Internet of Things (pp. 519-567). Cham: Springer International Publishing.
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., . . . Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354-377. doi:10.1016/j.patcog.2017.10.013
Han, J., Kamber, M., & Pei, J. (2012). 10 - Cluster Analysis: Basic Concepts and Methods. In J. Han, M. Kamber, & J. Pei (Eds.), Data Mining (Third Edition) (pp. 443-495). Boston: Morgan Kaufmann.
He, K., Zhang, X., Ren, S., & Sun, J. (2016, 27-30 June 2016). Deep Residual Learning for Image Recognition. Paper presented at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Head, S. W. (1954). Content Analysis of Television Drama Programs. The Quarterly of Film Radio and Television, 9(2), 175-194. doi:10.2307/1209974
Hiam, C. M., Berger, P. D., & Eshghi, G. (2017). Japan`s Millennials: The Minimalist Consumers of the Yutori / Satori Generation. International Journal of Business Insights & Transformation, 11(1), 4-8. Retrieved from https://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=129483547&lang=zh-tw&site=bsi-live
Hornby, A. S., & Deuter, M. (2015). Oxford Advanced Learner`s Dictionary of Current English: Oxford University Press.
Huang, G., Liu, Z., Maaten, L. V. D., & Weinberger, K. Q. (2017, 21-26 July 2017). Densely Connected Convolutional Networks. Paper presented at the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Huang, H.-Y., Shih, W.-S., & Hsu, W.-H. (2007). A Film Classifier Based on Low-level Visual Features (Vol. 3).
Iwabuchi, K. (2015). Pop-culture diplomacy in Japan: soft power, nation branding and the question of ‘international cultural exchange’. International Journal of Cultural Policy, 21(4), 419-432. doi:10.1080/10286632.2015.1042469
Jain, A. K., Jianchang, M., & Mohiuddin, K. M. (1996). Artificial neural networks: a tutorial. Computer, 29(3), 31-44. doi:10.1109/2.485891
Kam, T. H. (2013). Scripted affects, branded selves: television, subjectivity, and capitalism in 1990s Japan. Continuum, 27(5), 759-762. doi:10.1080/10304312.2013.780582
Kokol, P. (2009). Data-Mining and Knowledge Discovery, Introduction to. In R. A. Meyers (Ed.), Encyclopedia of Complexity and Systems Science (pp. 1810-1812). New York, NY: Springer New York.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
Kudo, S., & Yarime, M. (2013). Divergence of the sustaining and marginalizing communities in the process of rural aging: a case study of Yurihonjo-shi, Akita, Japan. Sustainability Science, 8(4), 491-513. doi:10.1007/s11625-012-0197-x
Kundalia, K., Patel, Y., & Shah, M. (2019). Multi-label Movie Genre Detection from a Movie Poster Using Knowledge Transfer Learning. Augmented Human Research, 5(1), 11. doi:10.1007/s41133-019-0029-y
Lee, S., Kc, B., & Choeh, J. Y. (2020). Comparing performance of ensemble methods in predicting movie box office revenue. Heliyon, 6(6), e04260. doi:10.1016/j.heliyon.2020.e04260
Lewis, D. D. (1998, 1998//). Naive (Bayes) at forty: The independence assumption in information retrieval. Paper presented at the Machine Learning: ECML-98, Berlin, Heidelberg.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Paper presented at the Proceedings of the fifth Berkeley symposium on mathematical statistics and probability.
Mandujano-Salazar, Y. Y. (2017). It is Not that I Can’t, It is that I Won’t: The Struggle of Japanese Women to Redefine Female Singlehood through Television Dramas. Asian Studies Review, 41(4), 526-543. doi:10.1080/10357823.2017.1371113
Mathur, M., & Chattopadhyay, A. (1991). The impact of moods generated by television programs on responses to advertising. Psychology & Marketing, 8(1), 59-77. doi:10.1002/mar.4220080106
Matsuzaki, Y., Okayasu, K., Imanari, T., Kobayashi, N., Kanehara, Y., Takasawa, R., . . . Kataoka, H. (2017, 8-12 May 2017). Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database. Paper presented at the 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA).
Morris, L. (2021). Watching more TV and movies is the nation’s favourite thing to do in lockdown. Retrieved from https://www.radiotimes.com/tv/drama/watching-tv-and-movies-favourite-lockdown-exclusive/
Nielsen. (2021). About. Retrieved from https://www.nielsentam.tv/aboutus/whatistam.asp
Ono, H. (2010). Lifetime employment in Japan: Concepts and measurements. Journal of the Japanese and International Economies, 24(1), 1-27. doi:10.1016/j.jjie.2009.11.003
Oxford Reference. (2021). cultivation theory. Retrieved from https://www.oxfordreference.com/view/10.1093/oi/authority.20110803095652677
Patel, J. M. (2020). Web Scraping in Python Using Beautiful Soup Library. In J. M. Patel (Ed.), Getting Structured Data from the Internet: Running Web Crawlers/Scrapers on a Big Data Production Scale (pp. 31-84). Berkeley, CA: Apress.
Potter, J. (1990). Drama. In In: Independent Television in Britain. London: Palgrave Macmillan.
Pujadas, G., & Muñoz, C. (2019). Extensive viewing of captioned and subtitled TV series: a study of L2 vocabulary learning by adolescents. The Language Learning Journal, 47(4), 479-496. doi:10.1080/09571736.2019.1616806
Refaeilzadeh, P., Tang, L., & Liu, H. (2009). Cross-Validation. In L. Liu & M. T. ÖZsu (Eds.), Encyclopedia of Database Systems (pp. 532-538). Boston, MA: Springer US.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65. doi:10.1016/0377-0427(87)90125-7
Rubenking, B., & Bracken, C. (2021). Binge watching and serial viewing: Comparing new media viewing habits in 2015 and 2020. Addictive Behaviors Reports, 14, 100356. doi:10.1016/j.abrep.2021.100356
Saito, S., & Ishiyama, R. (2005). The invisible minority: under‐representation of people with disabilities in prime‐time TV dramas in Japan. Disability & Society, 20(4), 437-451. doi:10.1080/09687590500086591
Scherer, E., & Thelen, T. (2020). On countryside roads to national identity: Japanese morning drama series (asadora) and contents tourism. Japan Forum, 32(1), 6-29. doi:10.1080/09555803.2017.1411378
scikit-learn. (2021a). 2.3. Clustering. Retrieved from https://scikit-learn.org/stable/modules/clustering.html#k-means
scikit-learn. (2021b). sklearn.preprocessing.OneHotEncoder. Retrieved from https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder
Sharda, R., & Delen, D. (2006). Predicting box-office success of motion pictures with neural networks. Expert Systems with Applications, 30(2), 243-254. doi:10.1016/j.eswa.2005.07.018
Sharma, H., & Kumar, S. N. (2016). A Survey on Decision Tree Algorithms of Classification in Data Mining.
Shi, Y., & Wang, T. (2019). Genuine Liking or the Need for Closure? The Differential Effects of Consumers’ TV Drama Viewing Motivations on Commercial Viewership. Journal of Media Economics, 32(3-4), 57-81. doi:10.1080/08997764.2021.1883916
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958.
Stibbe, A. (2004). Disability, gender and power in Japanese television drama. Japan Forum, 16(1), 21-36. doi:10.1080/0955580032000189311
Suzuki, H. (2010). Employment Relations in Japan: Recent Changes under Global Competition and Recession. Journal of Industrial Relations, 52(3), 387-401. doi:10.1177/0022185610365647
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016, 27-30 June 2016). Rethinking the Inception Architecture for Computer Vision. Paper presented at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Szegedy, C., Wei, L., Yangqing, J., Sermanet, P., Reed, S., Anguelov, D., . . . Rabinovich, A. (2015, 7-12 June 2015). Going deeper with convolutions. Paper presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Tadimari, A., Kumar, N., Guha, T., & Narayanan, S. S. (2016, 20-25 March 2016). Opening big in box office? Trailer content can help. Paper presented at the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Tamaki, T. (2019). Repackaging national identity: Cool Japan and the resilience of Japanese identity narratives. Asian Journal of Political Science, 27(1), 108-126. doi:10.1080/02185377.2019.1594323
Treme, J. (2010). Effects of Celebrity Media Exposure on Box-Office Performance. Journal of Media Economics, 23(1), 5-16. doi:10.1080/08997761003590457
Turner, A. (2015). Generation Z: Technology and Social Interest. The Journal of Individual Psychology, 71(2), 103-113. doi:10.1353/jip.2015.0021
Valaskivi, K. (2013). A brand new future? Cool Japan and the social imaginary of the branded nation. Japan Forum, 25(4), 485-504. doi:10.1080/09555803.2012.756538
Vapnik, V. (1999). The nature of statistical learning theory: Springer science & business media.
Venter, E. (2017). Bridging the communication gap between Generation Y and the Baby Boomer generation. International Journal of Adolescence and Youth, 22(4), 497-507. doi:10.1080/02673843.2016.1267022
Walter, E., & Cambridge University, P. (2005). Cambridge Advanced Learner`s Dictionary: Cambridge University Press.
Webb, G. I. (2010). Naïve Bayes. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning (pp. 713-714). Boston, MA: Springer US.
Wu, J. (2012). Cluster Analysis and K-means Clustering: An Introduction. In J. Wu (Ed.), Advances in K-means Clustering: A Data Mining Thinking (pp. 1-16). Berlin, Heidelberg: Springer Berlin Heidelberg.
Yamamura, T. (2015). Contents tourism and local community response: Lucky star and collaborative anime-induced tourism in Washimiya. Japan Forum, 27(1), 59-81. doi:10.1080/09555803.2014.962567
Zenith. (2021). TV advertising spending worldwide from 2000 to 2023, by region. Retrieved from https://www.statista.com/statistics/268666/tv-advertising-spending-worldwide-by-region/
Zhou, Y., Zhang, L., & Yi, Z. (2019). Predicting movie box-office revenues using deep neural networks. Neural Computing and Applications, 31(6), 1855-1865. doi:10.1007/s00521-017-3162-x
描述 碩士
國立政治大學
圖書資訊與檔案學研究所
109155002
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109155002
資料類型 thesis
dc.contributor.advisor 羅崇銘zh_TW
dc.contributor.advisor Lo, Chung-Mingen_US
dc.contributor.author (Authors) 徐子心zh_TW
dc.contributor.author (Authors) Syu, Zih-Sinen_US
dc.creator (作者) 徐子心zh_TW
dc.creator (作者) Syu, Zih-Sinen_US
dc.date (日期) 2022en_US
dc.date.accessioned 2-Sep-2022 14:58:32 (UTC+8)-
dc.date.available 2-Sep-2022 14:58:32 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2022 14:58:32 (UTC+8)-
dc.identifier (Other Identifiers) G0109155002en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141610-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 圖書資訊與檔案學研究所zh_TW
dc.description (描述) 109155002zh_TW
dc.description.abstract (摘要) 戲劇透過與社會現象或觀念的同步，讓觀眾對情節或角色產生共鳴，驅使人們產生觀看下一集的慾望，而反映人們收看熱度指標的收視率更是決定廣告收益與後續周邊經濟效益的參考標準。在文化、電視劇、收視率三者關係密切的情況下，本研究利用自2003年至2020年間800部日本黃金時段之電視劇，使用屬性特徵的年度、季度、電視台、星期、時間點、類型、編劇、原作、續集、演員的共10個特徵進行預測外，更加入海報中的人臉特徵以判別海報中人臉資訊對於收視率預測的重要性。比較簡易貝氏、類神經網路、支援向量機、隨機森林的4種分類器之預測結果後，加入人臉特徵的隨機森林模型之準確率由75.80%增加至77.10%，說明了人臉資訊對於收視率的整體預測有所貢獻。另一方面本研究也利用卷積神經網路模型，得知單獨使用海報影像時預測電視劇收視率之準確率為71.70%，說明了在卷積神經網路上使用海報影像預測電視劇收視率的可用性，並自研究結果探討影響收視率的因素以及反映這些因素的整體國家之文化內涵。zh_TW
dc.description.abstract (摘要) Drama through the synchronization with social phenomena or way of thinking, allows the audience to resonate with the plot or characters, and lets people to have the desire to watch the next episode. And the ratings of people’s viewing indicators, which can be the standard of advertising revenue and subsequent economic efficiency of surrounding areas. According to our research the relativity between culture, TV dramas and ratings is very high, in this study we use broadcast year, broadcast season, TV stations, day of the week, broadcast season, genre, screenwriters, original work, sequel, actor and face detection features of 800 Japanese TV dramas broadcasting during prime time to predict the ratings. After using four classifiers: Naïve Bayes, artificial neural network, support vector machine, and random forest, the accuracy of the random forest model with face detection features increased from 75.80% to 77.10%, which proves face information can improve the accuracy of the overall prediction ratings. On the other side, we use drama posters to predict ratings based on convolutional neural network, the accuracy is 71.70%, proves that the availability of using poster to predict ratings with the convolutional neural network. The experimental show the factors that affect ratings and the cultural connotation of the country that reflects these factors.en_US
dc.description.tableofcontents 摘要 i
Abstract ii
目次 iii
表次 v
圖次 vi
第一章 緒論 1
第一節 戲劇影響力 1
第二節 日本戲劇 2
第三節 戲劇與社會現象 5
第四節 戲劇收視率 10
第二章 文獻探討 13
第一節 使用屬性特徵預測 13
第二節 使用影像特徵預測 15
第三節 文獻探討總結與比較 17
第三章 研究方法 20
第一節 資料蒐集 21
壹、屬性蒐集 21
貳、海報蒐集 25
第二節 資料預處理 27
壹、資料正規化 27
第三節 模型訓練 29
壹、收視率分群 29
貳、機器學習 32
參、深度學習 36
第四章 研究結果 41
第一節 屬性資料之結果 41
壹、屬性資料之分布與分類 41
貳、屬性資料之機器學習 45
第二節 海報資料之結果 46
第五章 討論與結論 50
第六章 未來方向 53
參考文獻 55		zh_TW
dc.format.extent 2613862 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109155002en_US
dc.subject (關鍵詞) 收視率預測zh_TW
dc.subject (關鍵詞) 戲劇zh_TW
dc.subject (關鍵詞) 日本zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 深度學習zh_TW
dc.subject (關鍵詞) 電視劇海報zh_TW
dc.subject (關鍵詞) ratings predictionen_US
dc.subject (關鍵詞) dramaen_US
dc.subject (關鍵詞) Japanen_US
dc.subject (關鍵詞) machine learningen_US
dc.subject (關鍵詞) deep learningen_US
dc.subject (關鍵詞) drama posteren_US
dc.title (題名) 以資料探勘探討日本戲劇收視率影響要素與其文化內涵zh_TW
dc.title (題名) Exploring the Factors Influencing the Rating of Japanese Dramas and Its Cultural Connotation by Data Miningen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 日文文獻
TBSホールディングス(2022)。TBSテレビ。2022年1月3日，取自: https://www.tbs.co.jp/
TVer INC. (2022)。もっと、今をつなぐテレビへ。NOW ON TVer。2022年8月2日，取自: https://tver.jp/_s/campaign/nowontver/index.html
Video Research Ltd. (2021)。視聴率。2021年11月22 日，取自: https://www.videor.co.jp/service/media-data/tvrating.html
Nippon Television Network Corporation (2021)。日テレ広告ガイド。2021年11月25日，取自: https://ad.ntv.co.jp/guide/tvcm/index-spot2.html
ブリタニカジャパン株式会社 (2009)。ブリタニカ国際大百科事典:小項目版。東京:ロゴヴィスタ。
小学館 (1988)。日本大百科全書。東京:小学館。
中原美絵子(2014年3月14日)。日テレが、Hulu買収で仕掛ける「動画革命」。東洋経済。2021年10月30日，取自: https://toyokeizai.net/articles/-/32911
内閣府地方創生推進事務局 (2019)。まち・ひと・しごと創生長期ビジョン（令和元年改訂版）。2021年12月16日，取自: https://www.chisou.go.jp/sousei/info/pdf/r1-12-20-vision.pdf
内閣府景気統計部 (2021)。消費動向調査。2021年9月6日，取自: https://www.e-stat.go.jp/stat-search/file-download?statInfId=000032076871&fileKind=0
太田静 (2000)。私がみてる分, カウントされてますか?視聴率の調査方法。映像情報メディア学会誌，54(9)，1267-1268。doi:10.3169/itej.54.1267
日本国語大辞典第二版編集委員会、小学館国語辞典編集部、北原保雄 (2000)。日本国語大辞典 (第2版)。東京:小学館。
日本經濟新聞(2013年8月10日)。「あまちゃん」効果は32億円　岩手のシンクタンクが試算。日本經濟新聞。2021年10月24日，取自: https://www.nikkei.com/article/DGXNASDG1002W_Q3A810C1CR8000/
木村隆志 (2017年7月30日)。夏場のテレビ番組が迷走する理由 視聴習慣が乱れやすく制作側も迷い。ライブドアニュース。2021年6月23日，取自: https://news.livedoor.com/article/detail/13405190/
木村隆志 (2020年1月17日)。テレビドラマ｢刑事･医療系が75％｣の危険水域。東洋経済オンライン。2021年4月10日，取自: https://toyokeizai.net/articles/-/325250
北浦寛之 (2018)。テレビ成長期の日本映画:メディア間交渉のなかのドラマ。名古屋:名古屋大学出版会。
矢本成恒 (2008)。テレビ番組制作におけるエンジニアリング・ブランド。開発工学，28，27-30。doi:10.11363/kaihatsukogaku1984.28.27
佐藤裕 (2020年7月26日)。日曜劇場『半沢直樹』がコロナ禍の就活を変えるワケ。Yahoo!ニュース。2022年1月3日，取自: https://news.yahoo.co.jp/byline/yusato/20200726-00189950/
国土交通省総合政策局観光地域振興課、経済産業省商務情報政策局文化情報関連産業課、文化庁文化部芸術文化課(2005)。映像等コンテンツの制作・活用による地域振興のあり方に関する調査。2021年10月9日， 取自: http://www.mlit.go.jp/kokudokeikaku/souhatu/h16seika/12eizou/12eizou.htm
国立社会保障・人口問題研究所 (2017)。日本の将来推計人口。2021年11月1日，取自: http://www.ipss.go.jp/pp-zenkoku/j/zenkoku2017/pp29_gaiyou.pdf
香山リカ (2014年1月18日)。医療ものドラマはなぜウケるのか？。imidas。2022年6月26日，https://imidas.jp/josiki/?article_id=l-58-181-14-01-g320
產經新聞 (2017年2月23日)。「真田丸」の経済波及効果、長野では２００億９０００万円。產經新聞。2021年10月24日，取自: https://www.sankei.com/article/20170223-5G7Z5FEEG5JXFIHXDERWATDJ4A/
鳥山拡 (1993)。テレビドラマ⋅映画の世界(初版)。東京:早稲田大学出版社。
森晋也 (2020年10月24日)。4年間で164集落が消滅、人口減・高齢化で拍車。日本經濟新聞。2021年11月5日，取自: https://www.nikkei.com/article/DGXMZO65367920T21C20A0ML8000/
境治 (2017年12月27日)。世帯から個人へ､タイムシフトも反映。2018年､視聴率が変わる！。Yahoo!ニュース。2022年6月23日，取自: https://news.yahoo.co.jp/byline/sakaiosamu/20171227-00079793
福島悠介、山崎俊彦、相澤清晴 (2016)。放送前の情報のみを用いたテレビドラマの視聴率予測。映像情報メディア学会誌，70(11)，J255-J261。 doi:10.3169/itej.70.J255
総務省 (2019)。人口推計。2021年11月22日，取自: https://www.stat.go.jp/data/jinsui/2019np/pdf/2019np.pdf
総務省 (2020)。過疎地域等における集落の状況に関する現況把握調査報告書。2021年12月14日，取自: https://www.soumu.go.jp/main_content/000678497.pdf
総務省 (2021)。高齢者の人口。2021年11月1日，取自: https://www.stat.go.jp/data/topics/topi1291.html
総務省統計局 (2021)。令和3年労働力調査結果。2021年12月13日，取自:https://www.stat.go.jp/data/roudou/sokuhou/4hanki/dt/index.html
影山貴彦 (2019)。テレビドラマでわかる平成社会風俗史。東京 : 実業之日本社。
鎌田とし子、鎌田哲宏(2015)。「限界集落」における労働力の状態。日本労働社会学会年報，26，101-122. doi:10.20750/arls.arls026.101

英文文獻
Aboud, K. (2012). Medical dramas—the pros and the cons. Dermatology Practical & Conceptual, 2. doi:10.5826/dpc.0201a14
Adankon, M. M., & Cheriet, M. (2009). Support Vector Machine. In S. Z. Li & A. Jain (Eds.), Encyclopedia of Biometrics (pp. 1303-1308). Boston, MA: Springer US.
Agarwal, A., Das, R. R., & Das, A. (2021, 7-8 Oct. 2021). Machine Learning Techniques for Automated Movie Genre Classification Tool. Paper presented at the 2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE).
Agnes, M., & Guralnik, D. B. (2001). Webster`s New World college dictionary / Michael Agnes, editor in chief ; David B. Guralnik (4th ed.). New York: IDG Books Worldwide.
Ahn, J., Ma, K., Lee, O., & Sura, S. (2017). Do big data support TV viewing rate forecasting? A case study of a Korean TV drama. Information Systems Frontiers, 19(2), 411-420. doi:10.1007/s10796-016-9659-5
Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, 21-23 Aug. 2017). Understanding of a convolutional neural network. Paper presented at the 2017 International Conference on Engineering and Technology (ICET).
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., . . . Asari, V. K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164.
Araújo Vila, N., Fraiz Brea, J. A., & de Carlos, P. (2021). Film tourism in Spain: Destination awareness and visit motivation as determinants to visit places seen in TV series. European Research on Management and Business Economics, 27(1), 100135. doi:10.1016/j.iedeen.2020.100135
Arai, A., & Terano, T. (2005). Yutori Is Considered Harmful: Agent-Based Analysis for Education Policy in Japan. In R. Shiratori, K. Arai, & F. Kato (Eds.), Gaming, Simulations, and Society: Research Scope and Perspective (pp. 129-136). Tokyo: Springer Tokyo.
Biau, G., & Scornet, E. (2016). A random forest guided tour. TEST, 25(2), 197-227. doi:10.1007/s11749-016-0481-7
Bisong, E. (2019a). Ensemble Methods. In E. Bisong (Ed.), Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners (pp. 269-286). Berkeley, CA: Apress.
Bisong, E. (2019b). Introduction to Scikit-learn. In E. Bisong (Ed.), Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners (pp. 215-229). Berkeley, CA: Apress.
Bisong, E. (2019c). Support Vector Machines. In E. Bisong (Ed.), Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners (pp. 255-268). Berkeley, CA: Apress.
Boursier, V., Musetti, A., Gioia, F., Flayelle, M., Billieux, J., & Schimmenti, A. (2021). Is Watching TV Series an Adaptive Coping Strategy During the COVID-19 Pandemic? Insights From an Italian Community Sample. Frontiers in Psychiatry, 12(554). doi:10.3389/fpsyt.2021.599859
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. doi:10.1023/A:1010933404324
Bristi, W. R., Zaman, Z., & Sultana, N. (2019, 6-8 July 2019). Predicting IMDb Rating of Movies by Machine Learning Techniques. Paper presented at the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT).
Calzo, J. P., & Ward, L. M. (2009). Media Exposure and Viewers` Attitudes Toward Homosexuality: Evidence for Mainstreaming or Resonance? Journal of Broadcasting & Electronic Media, 53(2), 280-299. doi:10.1080/08838150902908049
Chang, B.-H., & Ki, E.-J. (2005). Devising a Practical Model for Predicting Theatrical Movie Success: Focusing on the Experience Good Property. Journal of Media Economics, 18(4), 247-269. doi:10.1207/s15327736me1804_2
Collins. (2021). prime time. Retrieved from https://www.collinsdictionary.com/dictionary/english/prime-time
Cross-Validation. (2009). In S. Z. Li & A. Jain (Eds.), Encyclopedia of Biometrics (pp. 206-206). Boston, MA: Springer US.
da Silva, I. N., Hernane Spatti, D., Andrade Flauzino, R., Liboni, L. H. B., & dos Reis Alves, S. F. (2017). Artificial Neural Network Architectures and Training Processes. In I. N. da Silva, D. Hernane Spatti, R. Andrade Flauzino, L. H. B. Liboni, & S. F. dos Reis Alves (Eds.), Artificial Neural Networks : A Practical Course (pp. 21-28). Cham: Springer International Publishing.
Danaher, P., & Dagger, T. (2012). Using a nested logit model to forecast television ratings. International Journal of Forecasting, 28(3), 607-622. doi:10.1016/j.ijforecast.2012.02.008
Dissanayake, W. (2012). Asian television dramas and Asian theories of communication. Journal of Multicultural Discourses, 7(2), 191-196. doi:10.1080/17447143.2012.666246
Fürnkranz, J. (2010). Decision Tree. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning (pp. 263-267). Boston, MA: Springer US.
Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). Knowledge Discovery and Data Mining: Towards a Unifying Framework.
Ghosh, A., Sufian, A., Sultana, F., Chakrabarti, A., & De, D. (2020). Fundamental Concepts of Convolutional Neural Network. In V. E. Balas, R. Kumar, & R. Srivastava (Eds.), Recent Trends and Advances in Artificial Intelligence and Internet of Things (pp. 519-567). Cham: Springer International Publishing.
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., . . . Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354-377. doi:10.1016/j.patcog.2017.10.013
Han, J., Kamber, M., & Pei, J. (2012). 10 - Cluster Analysis: Basic Concepts and Methods. In J. Han, M. Kamber, & J. Pei (Eds.), Data Mining (Third Edition) (pp. 443-495). Boston: Morgan Kaufmann.
He, K., Zhang, X., Ren, S., & Sun, J. (2016, 27-30 June 2016). Deep Residual Learning for Image Recognition. Paper presented at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Head, S. W. (1954). Content Analysis of Television Drama Programs. The Quarterly of Film Radio and Television, 9(2), 175-194. doi:10.2307/1209974
Hiam, C. M., Berger, P. D., & Eshghi, G. (2017). Japan`s Millennials: The Minimalist Consumers of the Yutori / Satori Generation. International Journal of Business Insights & Transformation, 11(1), 4-8. Retrieved from https://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=129483547&lang=zh-tw&site=bsi-live
Hornby, A. S., & Deuter, M. (2015). Oxford Advanced Learner`s Dictionary of Current English: Oxford University Press.
Huang, G., Liu, Z., Maaten, L. V. D., & Weinberger, K. Q. (2017, 21-26 July 2017). Densely Connected Convolutional Networks. Paper presented at the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Huang, H.-Y., Shih, W.-S., & Hsu, W.-H. (2007). A Film Classifier Based on Low-level Visual Features (Vol. 3).
Iwabuchi, K. (2015). Pop-culture diplomacy in Japan: soft power, nation branding and the question of ‘international cultural exchange’. International Journal of Cultural Policy, 21(4), 419-432. doi:10.1080/10286632.2015.1042469
Jain, A. K., Jianchang, M., & Mohiuddin, K. M. (1996). Artificial neural networks: a tutorial. Computer, 29(3), 31-44. doi:10.1109/2.485891
Kam, T. H. (2013). Scripted affects, branded selves: television, subjectivity, and capitalism in 1990s Japan. Continuum, 27(5), 759-762. doi:10.1080/10304312.2013.780582
Kokol, P. (2009). Data-Mining and Knowledge Discovery, Introduction to. In R. A. Meyers (Ed.), Encyclopedia of Complexity and Systems Science (pp. 1810-1812). New York, NY: Springer New York.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
Kudo, S., & Yarime, M. (2013). Divergence of the sustaining and marginalizing communities in the process of rural aging: a case study of Yurihonjo-shi, Akita, Japan. Sustainability Science, 8(4), 491-513. doi:10.1007/s11625-012-0197-x
Kundalia, K., Patel, Y., & Shah, M. (2019). Multi-label Movie Genre Detection from a Movie Poster Using Knowledge Transfer Learning. Augmented Human Research, 5(1), 11. doi:10.1007/s41133-019-0029-y
Lee, S., Kc, B., & Choeh, J. Y. (2020). Comparing performance of ensemble methods in predicting movie box office revenue. Heliyon, 6(6), e04260. doi:10.1016/j.heliyon.2020.e04260
Lewis, D. D. (1998, 1998//). Naive (Bayes) at forty: The independence assumption in information retrieval. Paper presented at the Machine Learning: ECML-98, Berlin, Heidelberg.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Paper presented at the Proceedings of the fifth Berkeley symposium on mathematical statistics and probability.
Mandujano-Salazar, Y. Y. (2017). It is Not that I Can’t, It is that I Won’t: The Struggle of Japanese Women to Redefine Female Singlehood through Television Dramas. Asian Studies Review, 41(4), 526-543. doi:10.1080/10357823.2017.1371113
Mathur, M., & Chattopadhyay, A. (1991). The impact of moods generated by television programs on responses to advertising. Psychology & Marketing, 8(1), 59-77. doi:10.1002/mar.4220080106
Matsuzaki, Y., Okayasu, K., Imanari, T., Kobayashi, N., Kanehara, Y., Takasawa, R., . . . Kataoka, H. (2017, 8-12 May 2017). Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database. Paper presented at the 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA).
Morris, L. (2021). Watching more TV and movies is the nation’s favourite thing to do in lockdown. Retrieved from https://www.radiotimes.com/tv/drama/watching-tv-and-movies-favourite-lockdown-exclusive/
Nielsen. (2021). About. Retrieved from https://www.nielsentam.tv/aboutus/whatistam.asp
Ono, H. (2010). Lifetime employment in Japan: Concepts and measurements. Journal of the Japanese and International Economies, 24(1), 1-27. doi:10.1016/j.jjie.2009.11.003
Oxford Reference. (2021). cultivation theory. Retrieved from https://www.oxfordreference.com/view/10.1093/oi/authority.20110803095652677
Patel, J. M. (2020). Web Scraping in Python Using Beautiful Soup Library. In J. M. Patel (Ed.), Getting Structured Data from the Internet: Running Web Crawlers/Scrapers on a Big Data Production Scale (pp. 31-84). Berkeley, CA: Apress.
Potter, J. (1990). Drama. In In: Independent Television in Britain. London: Palgrave Macmillan.
Pujadas, G., & Muñoz, C. (2019). Extensive viewing of captioned and subtitled TV series: a study of L2 vocabulary learning by adolescents. The Language Learning Journal, 47(4), 479-496. doi:10.1080/09571736.2019.1616806
Refaeilzadeh, P., Tang, L., & Liu, H. (2009). Cross-Validation. In L. Liu & M. T. ÖZsu (Eds.), Encyclopedia of Database Systems (pp. 532-538). Boston, MA: Springer US.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65. doi:10.1016/0377-0427(87)90125-7
Rubenking, B., & Bracken, C. (2021). Binge watching and serial viewing: Comparing new media viewing habits in 2015 and 2020. Addictive Behaviors Reports, 14, 100356. doi:10.1016/j.abrep.2021.100356
Saito, S., & Ishiyama, R. (2005). The invisible minority: under‐representation of people with disabilities in prime‐time TV dramas in Japan. Disability & Society, 20(4), 437-451. doi:10.1080/09687590500086591
Scherer, E., & Thelen, T. (2020). On countryside roads to national identity: Japanese morning drama series (asadora) and contents tourism. Japan Forum, 32(1), 6-29. doi:10.1080/09555803.2017.1411378
scikit-learn. (2021a). 2.3. Clustering. Retrieved from https://scikit-learn.org/stable/modules/clustering.html#k-means
scikit-learn. (2021b). sklearn.preprocessing.OneHotEncoder. Retrieved from https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder
Sharda, R., & Delen, D. (2006). Predicting box-office success of motion pictures with neural networks. Expert Systems with Applications, 30(2), 243-254. doi:10.1016/j.eswa.2005.07.018
Sharma, H., & Kumar, S. N. (2016). A Survey on Decision Tree Algorithms of Classification in Data Mining.
Shi, Y., & Wang, T. (2019). Genuine Liking or the Need for Closure? The Differential Effects of Consumers’ TV Drama Viewing Motivations on Commercial Viewership. Journal of Media Economics, 32(3-4), 57-81. doi:10.1080/08997764.2021.1883916
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958.
Stibbe, A. (2004). Disability, gender and power in Japanese television drama. Japan Forum, 16(1), 21-36. doi:10.1080/0955580032000189311
Suzuki, H. (2010). Employment Relations in Japan: Recent Changes under Global Competition and Recession. Journal of Industrial Relations, 52(3), 387-401. doi:10.1177/0022185610365647
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016, 27-30 June 2016). Rethinking the Inception Architecture for Computer Vision. Paper presented at the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Szegedy, C., Wei, L., Yangqing, J., Sermanet, P., Reed, S., Anguelov, D., . . . Rabinovich, A. (2015, 7-12 June 2015). Going deeper with convolutions. Paper presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Tadimari, A., Kumar, N., Guha, T., & Narayanan, S. S. (2016, 20-25 March 2016). Opening big in box office? Trailer content can help. Paper presented at the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Tamaki, T. (2019). Repackaging national identity: Cool Japan and the resilience of Japanese identity narratives. Asian Journal of Political Science, 27(1), 108-126. doi:10.1080/02185377.2019.1594323
Treme, J. (2010). Effects of Celebrity Media Exposure on Box-Office Performance. Journal of Media Economics, 23(1), 5-16. doi:10.1080/08997761003590457
Turner, A. (2015). Generation Z: Technology and Social Interest. The Journal of Individual Psychology, 71(2), 103-113. doi:10.1353/jip.2015.0021
Valaskivi, K. (2013). A brand new future? Cool Japan and the social imaginary of the branded nation. Japan Forum, 25(4), 485-504. doi:10.1080/09555803.2012.756538
Vapnik, V. (1999). The nature of statistical learning theory: Springer science & business media.
Venter, E. (2017). Bridging the communication gap between Generation Y and the Baby Boomer generation. International Journal of Adolescence and Youth, 22(4), 497-507. doi:10.1080/02673843.2016.1267022
Walter, E., & Cambridge University, P. (2005). Cambridge Advanced Learner`s Dictionary: Cambridge University Press.
Webb, G. I. (2010). Naïve Bayes. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning (pp. 713-714). Boston, MA: Springer US.
Wu, J. (2012). Cluster Analysis and K-means Clustering: An Introduction. In J. Wu (Ed.), Advances in K-means Clustering: A Data Mining Thinking (pp. 1-16). Berlin, Heidelberg: Springer Berlin Heidelberg.
Yamamura, T. (2015). Contents tourism and local community response: Lucky star and collaborative anime-induced tourism in Washimiya. Japan Forum, 27(1), 59-81. doi:10.1080/09555803.2014.962567
Zenith. (2021). TV advertising spending worldwide from 2000 to 2023, by region. Retrieved from https://www.statista.com/statistics/268666/tv-advertising-spending-worldwide-by-region/
Zhou, Y., Zhang, L., & Yi, Z. (2019). Predicting movie box-office revenues using deep neural networks. Neural Computing and Applications, 31(6), 1855-1865. doi:10.1007/s00521-017-3162-x		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202201197en_US