請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74389
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 曹承礎(Seng-Cho Chou) | |
dc.contributor.author | Lung-Hsiung Chen | en |
dc.contributor.author | 陳隆翔 | zh_TW |
dc.date.accessioned | 2021-06-17T08:33:11Z | - |
dc.date.available | 2024-06-24 | |
dc.date.copyright | 2019-08-20 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-09 | |
dc.identifier.citation | APALA, K. R., JOSE, M., MOTNAM, S., CHAN, C.-C., LISZKA, K. J. & DE GREGORIO, F. Prediction of movies box office performance using social media. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2013. ACM, 1209-1214.
BREIMAN, L. J. M. L. 2001. Random forests. 45, 5-32. CHA, M., KWAK, H., RODRIGUEZ, P., AHN, Y.-Y. & MOON, S. 2007. I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system. Proceedings of the 7th ACM SIGCOMM conference on Internet measurement. San Diego, California, USA: ACM. CHEN, P.-L., TSAI, C.-T., CHEN, Y.-N., CHOU, K.-C., LI, C.-L., TSAI, C.-H., WU, K.-W., CHOU, Y.-C., LI, C.-Y. & LIN, W.-S. A linear ensemble of individual and blended models for music rating prediction. Proceedings of KDD Cup 2011, 2012. 21-60. CUNNINGHAM, S. J. & NICHOLS, D. M. 2008. How people find videos. Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries. Pittsburgh PA, PA, USA: ACM. EDMOND, M. 2014. Here We Go Again:Music Videos after YouTube. 15, 305-320. FIGUEIREDO, F., BENEVENUTO, F. & ALMEIDA, J. M. The tube over time: characterizing popularity growth of youtube videos. Proceedings of the fourth ACM international conference on Web search and data mining, 2011. ACM, 745-754. HOERL, A. E. & KENNARD, R. W. J. T. 1970. Ridge regression: Biased estimation for nonorthogonal problems. 12, 55-67. KHAN, M. L. J. C. I. H. B. 2017. Social media engagement: What motivates user participation and consumption on YouTube? 66, 236-247. LEE, J. & LEE, J.-S. Predicting music popularity patterns based on musical complexity and early stage popularity. Proceedings of the Third Edition Workshop on Speech, Language & Audio in Multimedia, 2015. ACM, 3-6. NONNECKE, B., PREECE, J., ANDREWS, D. & VOUTOUR, R. J. A. P. 2004. Online lurkers tell why. 321. PINTO, H., ALMEIDA, J. M. & GONÇALVES, M. A. Using early view patterns to predict the popularity of youtube videos. Proceedings of the sixth ACM international conference on Web search and data mining, 2013. ACM, 365-374. RAHIM, M. S., CHOWDHURY, A. E., ISLAM, M. A. & ISLAM, M. R. Mining trailers data from youtube for predicting gross income of movies. 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 2017. IEEE, 551-554. ROY, S. D., MEI, T., ZENG, W. & LI, S. J. I. T. O. M. 2013. Towards cross-domain learning for social video popularity prediction. 15, 1255-1267. SCHOEFFLER, M. & HERRE, J. The influence of audio quality on the popularity of music videos: A YouTube case study. Proceedings of the First International Workshop on Internet-Scale Multimedia Management, 2014. ACM, 35-38. SZABO, G. & HUBERMAN, B. A. J. A. A. S. 2008. Predicting the popularity of online content. TAKAHASHI, M., FUJIMOTO, M. & YAMASAKI, N. The active lurker: influence of an in-house online community on its outside environment. Proceedings of the 2003 international ACM SIGGROUP conference on Supporting group work, 2003. ACM, 1-10. TATAR, A., DE AMORIM, M. D., FDIDA, S., ANTONIADIS, P. J. J. O. I. S. & APPLICATIONS 2014. A survey on predicting the popularity of web content. 5, 8. THELWALL, M., SUD, P., VIS, F. J. J. O. T. A. S. F. I. S. & TECHNOLOGY 2012. Commenting on YouTube videos: From Guatemalan rock to el big bang. 63, 616-629. TRZCIŃSKI, T. & ROKITA, P. J. I. T. O. M. 2017. Predicting popularity of online videos using support vector regression. 19, 2561-2570. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74389 | - |
dc.description.abstract | 隨著近年台灣用戶影音視聽的習慣轉變,實體唱片業的利潤逐漸下滑,而數位音樂利潤逐年上升,甚至串流服務有了高幅度的增長。而像是YouTube平台等影音媒體平台,成為人們取得娛樂與資訊的重要平台,目前許多音樂公司亦會選擇在歌曲發佈時,同時將音樂影音( MV, Music Video) 上傳至YouTube,使觀眾能在最便利的管道,得到最佳的觀影品質,也就是影像與聲音的雙重體驗,進而促進未來可能更多深入的收聽行動。
而隨著串流影音的盛行,在影音平台上取得成功的用戶體驗,並轉換成具收益的下載量,對於音樂公司來說,是在歌曲表現的重要指標,而YouTube作為最容易、同時也是最可能是聽眾第一次接觸該音樂作品的第一管道,本研究透過分析YouTube官方音樂錄影帶的資料,預測在Spotify上的串流流量,並瞭解何種因子可能會與此轉換有關,提供未來相關人員進行深入研究,或作為行銷規劃的決策依據。 | zh_TW |
dc.description.abstract | In recent years, the habits of watching media in Taiwan have been changing, media platforms such as YouTube have become an important platform for people to get entertainment and information. The profits of the physical musice recording industry have gradually declined, while the profits of digital music have increased year by year and even the streaming services have experienced a signicicant growth. At present, many music media companies will choose to upload MV (Music Video) to YouTube when the song recordings are released, so that viewers can experience the best viewing quality, that is, the dual experience of visual and audio, and thus become a potential motivation for their further experience with the music or singer.
With the prevalence of streaming media platform, achieving a successful user experience on the audio-visual platform and converting it into a revenue-generating download is an important indicator of song performance for music companies, and YouTube is the easiest way for listener to have first interaction with the music or singer. Our study researcher on YouTube's official music video data, attempting to predict streaming traffic volume on Spotify, and to figure out which factors may be relevant to this conversion. In the future, relevant personnel can conduct a further research into it and conduct their decision on marketing strategy based on our results. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:33:11Z (GMT). No. of bitstreams: 1 ntu-108-R05725027-1.pdf: 4129971 bytes, checksum: 3135b120f54bed92642532ba5b41b1aa (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 謝辭 i
中文摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vi 表目錄 vii Chapter 1 緒論 1 Chapter 2 文獻回顧 4 2.1 媒體使用習慣與YouTube趨勢 4 2.2 YouTube音樂趨勢與熱門影片特徵 4 2.3 Youtube流量預測與其他相關研究 6 2.4 影響觀賞音樂影片體驗之因子 7 Chapter 3 實驗架構與研究方法 10 3.1 整體系統架構 10 3.2 資料收集與資料前處理 13 3.3 預測模型 14 3.4 模型評估方式與結果 17 3.5 資料簡介 18 Chapter 4 實際實驗與資料分析結果 20 4.1 爬蟲 20 4.2 API 24 4.3 特徵增減、資料補值與前處理 26 4.4 模型實際套入 31 4.5 特徵因子分析 34 Chapter 5 結論 37 REFERENCE 38 | |
dc.language.iso | zh-TW | |
dc.title | 預測音樂於串流平台上之表現:以YouTube影音與Spotify資料為例 | zh_TW |
dc.title | Predicting Performance on Media Service: Analyzing Music Video Data on YouTube | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 吳玲玲(Ling-Ling Wu) | |
dc.contributor.oralexamcommittee | 周子元 | |
dc.subject.keyword | 音樂錄影帶,流量預測,影片特徵,迴歸模型, | zh_TW |
dc.subject.keyword | Music Video,Prediction,Video Feature,Regression Model, | en |
dc.relation.page | 39 | |
dc.identifier.doi | 10.6342/NTU201902981 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-12 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-108-1.pdf 目前未授權公開取用 | 4.03 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。