政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141610

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141610

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题名:	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
贡献者:	羅崇銘 Lo, Chung-Ming 徐子心 Syu, Zih-Sin
关键词:	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
上传时间:	2022-09-02 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.
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