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題名 Analyzing drama metadata through machine learning to gain insights into social information dissemination patterns 作者 羅崇銘
Lo, Chung-Ming;Syu, Zih-Sin貢獻者 圖檔所 日期 2023-11 上傳時間 10-Apr-2024 16:26:52 (UTC+8) 摘要 TV drama, through synchronization with social phenomena, allows the audience to resonate with the characters and desire to watch the next episode. In particular, drama ratings can be the criterion for advertisers to invest in ad placement and a predictor of subsequent economic efficiency in the surrounding areas. To identify the dissemination patterns of social information about dramas, this study used machine learning to predict drama ratings and the contribution of various drama metadata, including broadcast year, broadcast season, TV stations, day of the week, broadcast time slot, genre, screenwriters, status as an original work or sequel, actors and facial features on posters. A total of 800 Japanese TV dramas broadcast during prime time between 2003 and 2020 were collected for analysis. Four machine learning classifiers, including naïve Bayes, artificial neural network, support vector machine, and random forest, were used to combine the metadata. With facial features, the accuracy of the random forest model increased from 75.80% to 77.10%, which shows that poster information can improve the accuracy of the overall predicted ratings. Using only posters to predict ratings with a convolutional neural network still obtained an accuracy rate of 71.70%. More insights about the correlations between drama metadata and social information dissemination patterns were explored. 關聯 PLOS One, Vol.18, No.11, e0288932 資料類型 article DOI https://doi.org/10.1371/journal.pone.0288932 dc.contributor 圖檔所 dc.creator (作者) 羅崇銘 dc.creator (作者) Lo, Chung-Ming;Syu, Zih-Sin dc.date (日期) 2023-11 dc.date.accessioned 10-Apr-2024 16:26:52 (UTC+8) - dc.date.available 10-Apr-2024 16:26:52 (UTC+8) - dc.date.issued (上傳時間) 10-Apr-2024 16:26:52 (UTC+8) - dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/150694 - dc.description.abstract (摘要) TV drama, through synchronization with social phenomena, allows the audience to resonate with the characters and desire to watch the next episode. In particular, drama ratings can be the criterion for advertisers to invest in ad placement and a predictor of subsequent economic efficiency in the surrounding areas. To identify the dissemination patterns of social information about dramas, this study used machine learning to predict drama ratings and the contribution of various drama metadata, including broadcast year, broadcast season, TV stations, day of the week, broadcast time slot, genre, screenwriters, status as an original work or sequel, actors and facial features on posters. A total of 800 Japanese TV dramas broadcast during prime time between 2003 and 2020 were collected for analysis. Four machine learning classifiers, including naïve Bayes, artificial neural network, support vector machine, and random forest, were used to combine the metadata. With facial features, the accuracy of the random forest model increased from 75.80% to 77.10%, which shows that poster information can improve the accuracy of the overall predicted ratings. Using only posters to predict ratings with a convolutional neural network still obtained an accuracy rate of 71.70%. More insights about the correlations between drama metadata and social information dissemination patterns were explored. dc.format.extent 108 bytes - dc.format.mimetype text/html - dc.relation (關聯) PLOS One, Vol.18, No.11, e0288932 dc.title (題名) Analyzing drama metadata through machine learning to gain insights into social information dissemination patterns dc.type (資料類型) article dc.identifier.doi (DOI) 10.1371/journal.pone.0288932 dc.doi.uri (DOI) https://doi.org/10.1371/journal.pone.0288932
