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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.
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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
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dc.identifier.doi (DOI) 10.6814/NCCU202201197en_US