博碩士論文 111423028 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:56 、訪客IP:18.219.103.10
姓名 張芮瑄(Jui-Hsuan Chang)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 整合 SOR 模型與情感分析於 YouTube 頻道之社會影響力管理策略分析
(Integrating SOR Model and Sentiment Analysis to Analyze Social Influence Management Strategy of YouTube Channels)
相關論文
★ 以機器學習技術為基礎建構新生兒孕育健康狀態預測模型★ 電子病歷縮寫消歧與一對多分類任務
★ 使用文字探勘與深度學習技術建置中風後肺炎之預測模型★ 精準社群廣告投資策略:以機器學習技術為基礎之社會影響力管理模式
★ 智慧活躍老化之實現:以資料驅動為基礎之AI長者在地交友推薦模式★ 整合深度學習技術與SOR理論之資訊情緒傳遞性探索:新聞生成特質與資訊情感傳播行為
★ 基於混合過濾的電影推薦系統★ 以 Reddit 使用者生成內容探討糖尿病照護社會支持
★ 防範於未然:基於機器學習技術之網路入侵偵測系統★ 可靠度驗證實驗室導入人工智慧技術的可行性探討 -以A公司為例
★ 智慧共同照護之實現: 以資料驅動為基礎之 AI 糖尿病個案管理模式★ Non-Touch Cooperation: An Interactive Mechanism Design Based on Mid-Air Gestures
★ 基於 UGC 的反脆弱社交口碑策略:以臺灣飯店業為例★ 基於機器學習技術之誘導式評論過濾機制:以餐廳評論為例
★ 資料驅動的球隊經營:NBA球隊競爭力與抱團策略之研究★ 基於顧客評論觀點揭示組織調整策略和顧客體驗 優化:以 Covid-19 餐飲業為例
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2029-8-21以後開放)
摘要(中) 隨著社群媒體成為大眾接收新資訊的主要管道之一,品牌的社會影響力管理相較於以往變得更重要。許多企業和YouTuber在YouTube平台上進行內容行銷,希望提升品牌的社會影響力。這不僅能夠影響觀眾的購買決策,還可以提升品牌忠誠度。目前關於社會影響力的研究很少關注不同的YouTube頻道管理策略對社會影響力的影響。對於那些想要增加社會影響力的企業或個人品牌來說,缺乏一些明確的商業策略建議。本研究建立了新的YouTube資料集,結合情緒分析,將SOR模型應用於社會影響力管理策略,以社群品牌屬性為刺激(S),觀眾情緒為有機體(O),訂閱數成長率為反應(R),分析YouTube頻道管理策略對觀眾情緒的影響,以及觀眾情緒對社會影響力的影響。此外,也分析了企業品牌和個人品牌之社群頻道管理策略差異。結果顯示頻道年齡和影片標題情緒對觀眾情緒有顯著的影響,並且觀眾情緒對於社群影響力有顯著的影響。最後,這項研究為想要增加線上社會影響力的品牌提供了 YouTube 商業策略的明確建議。
摘要(英) As social media has become one of the main channels for the public to receive new information, brand social influence management is more important than in the past. Many companies and YouTubers conduct content marketing on the YouTube platform, hoping to enhance the brand′s social influence. This can not only influence the audience’s purchasing decisions, but also enhance brand loyalty. Current research on social influence rarely pays attention to the impact of different YouTube channel management strategies on social influence. For those businesses or personal brands that want to increase their social influence, they lack some clear business strategy suggestions. This study establishes a new YouTube dataset, combines sentiment analysis, and applies the SOR model to social influence management strategies, using social brand attributes as stimuli (S), audience emotions as organisms (O), and subscription growth rate as reaction (R), analyze the impact of YouTube channel management strategies on audience emotions, and the impact of audience emotions on social influence. In addition, the difference of social channel management strategies between corporate brands and personal brands was also analyzed. The results show that channel age and sentiment of videos title have significant impact on audience emotions. And audience emotions have significant impact on social influence. Finally, this study provides clear suggestions for brands that want to increase their social influence.
關鍵字(中) ★ YouTube
★ 社群品牌
★ 社會影響力
★ 情感分析
★ SOR 模型
關鍵字(英) ★ YouTube
★ Social Branding
★ Social Influence
★ Sentiment Analysis
★ SOR Model
論文目次 Chinese Abstract................................i
English Abstract...............................ii
Acknowledgements..............................iii
Table of Contents..............................iv
List of Figures................................vi
List of Tables................................vii
I. Introduction............................1
1-1 Research Background and Motivation......1
1-1-1 Content Marketing on Social Media.......1
1-1-2 Social Influence on YouTube.............3
1-1-3 User Generated Content..................4
1-1-4 SOR Model...............................5
1-2 Research Objectives.....................6
1-3 Research Structure......................8
II. Literature Review......................10
2-1 Social Influence.......................10
2-2 Sentiment Analysis.....................15
2-2-1 Dictionary-based.......................15
2-2-2 Machine Learning-based.................16
2-2-3 Deep Learning-based....................16
2-3 SOR Model..............................18
III. Methodology............................20
3-1 Research Framework.....................20
3-2 Data collection........................25
3-3 Data preprocessing.....................33
3-4 Sentiment Analysis.....................36
IV. Research Results and Discussion........37
4-1 Descriptive Statistical Analysis Results.37
4-2 Simple Regression Analysis.............41
4-3 Structural Equation Modeling Analysis..46
4-3-1 Bootstrap Method.......................46
4-3-2 Moderating Effect Analysis of Brand Type.47
V. Research Conclusion and Contribution...48
5-1 Conclusion and Contribution............48
5-2 Limitation and Future Research.........49
VI. Reference..............................50
參考文獻 Ali, N., Hamid, M., & Youssif, A. (2019). Sentiment Analysis for Movies Reviews Dataset Using Deep Learning Models. International Journal of Data Mining & Knowledge Management Process, 09, 19–27. https://doi.org/10.5121/ijdkp.2019.9302
Auxier, B., & Anderson, M. (2021). Social Media Use in 2021. Pew Research Center, 1(1), 1–4.
Baber, R., & Baber, P. (2023). Influence of Social Media Marketing Efforts, E-reputation and Destination Image on Intention to Visit Among Tourists: Application of S-O-R Model. Journal of Hospitality and Tourism Insights, 6(5), 2298–2316. https://doi.org/10.1108/JHTI-06-2022-0270
Barbieri, F., Camacho-Collados, J., Neves, L., & Espinosa-Anke, L. (2020). TweetEval: Unified benchmark and comparative evaluation for tweet classification. arXiv Preprint arXiv:2010.12421.
Belanche, D., Flavián, M., & Ibáñez-Sánchez, S. (2020). Followers’ Reactions to Influencers’ Instagram Posts. Spanish Journal of Marketing-ESIC, 24(1), 37–54.
Book, L. A., Tanford, S., Montgomery, R., & Love, C. (2018). Online Traveler Reviews as Social Influence: Price Is No Longer King. Journal of Hospitality & Tourism Research, 42(3), 445–475. https://doi.org/10.1177/1096348015597029
Calder, B. J. (2022). Customer Interaction Strategy, Brand Purpose and Brand Communities. Journal of Service Management, 33(4/5), 747–757. https://doi.org/10.1108/JOSM-11-2021-0410
Chau, C. (2010). YouTube as a Participatory Culture. New Directions for Youth Development, 2010(128), 65–74. https://doi.org/10.1002/yd.376
Chen, R., Peng, L., & Qin, Y. (2010). Supermarket Shopping Guide System Based on Internet of Things.
Christodoulides, G., Jevons, C., & Bonhomme, J. (2012). Memo to Marketers: Quantitative Evidence for Change How User-Generated Content Really Affects Brands. Journal of Advertising Research, 52. https://doi.org/10.2501/JAR-52-1-053-064
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. North American Chapter of the Association for Computational Linguistics. https://api.semanticscholar.org/CorpusID:52967399
Dewan, S., Ho, Y.-J. (Ian), & Ramaprasad, J. (2017). Popularity or Proximity: Characterizing the Nature of Social Influence in an Online Music Community. Information Systems Research, 28(1), 117–136. https://doi.org/10.1287/isre.2016.0654
Djenouri, Y., Belhadi, A., Srivastava, G., & Lin, J. C.-W. (2022). Deep Learning Based Hashtag Recommendation System for Multimedia Data. Information Sciences, 609, 1506–1517.
Erdoğmuş, İ. E., & Çiçek, M. (2012). The Impact of Social Media Marketing on Brand Loyalty. 8th International Strategic Management Conference, 58, 1353–1360. https://doi.org/10.1016/j.sbspro.2012.09.1119
Gatautis, R., Vitkauskaitė, E., Gadeikiene, A., & Piligrimienė, Ž. (2016). Gamification as a Mean of Driving Online Consumer Behaviour: SOR Model Perspective. Engineering Economics, 27. https://doi.org/10.5755/j01.ee.27.1.13198
Go, A., Bhayani, R., & Huang, L. (2009). Twitter Sentiment Classification Using Distant Supervision. Processing, 150.
Goldenberg, A., & Gross, J. J. (2020). Digital Emotion Contagion. Trends in Cognitive Sciences, 24(4), 316–328.
Gong, Q., Chen, Y., He, X., Xiao, Y., Hui, P., Wang, X., & Fu, X. (2021). Cross-site Prediction on Social Influence for Cold-start Users in Online Social Networks. ACM Trans. Web, 15(2). https://doi.org/10.1145/3409108
H. . -M. Gross, H. Boehme, C. Schroeter, S. Mueller, A. Koenig, E. Einhorn, C. Martin, M. Merten, & A. Bley. (2009). TOOMAS: Interactive Shopping Guide Robots in Everyday Use—Final Implementation and Experiences from Long-term Field Trials. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005–2012. https://doi.org/10.1109/IROS.2009.5354497
Hewei, T., & Youngsook, L. (2022). Factors Affecting Continuous Purchase Intention of Fashion Products on Social E-commerce: SOR Model and the Mediating Effect. Entertainment Computing, 41, 100474. https://doi.org/10.1016/j.entcom.2021.100474
Hollebeek, L. (2011). Exploring Customer Brand Engagement: Definition and Themes. Journal of Strategic Marketing, 19(7), 555–573. https://doi.org/10.1080/0965254X.2011.599493
Hou, Z., & Choi, C. (2019). Research on Influencing Factors of YouTube Chinese Video User Subscription Motivation: Centered on the Censydiam User Motivation Analysis model. International Journal of Internet, Broadcasting and Communication, 11(3), 95–105.
Hruska, J., & Maresova, P. (2020). Use of Social Media Platforms among Adults in the United States—Behavior on Social Media. Societies, 10(1). https://doi.org/10.3390/soc10010027
Huffaker, D. (2010). Dimensions of Leadership and Social Influence in Online Communities. Human Communication Research, 36(4), 593–617. https://doi.org/10.1111/j.1468-2958.2010.01390.x
Hutto, C., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550
Jahin, M. A., Shovon, M. S. H., & Mridha, M. (2024). TRABSA: Interpretable Sentiment Analysis of Tweets using Attention-based BiLSTM and Twitter-RoBERTa. arXiv Preprint arXiv:2404.00297.
Jain, P. K., Pamula, R., & Srivastava, G. (2021). A Systematic Literature Review on Machine Learning Applications for Consumer Sentiment Analysis Using Online Reviews. Computer Science Review, 41, 100413. https://doi.org/10.1016/j.cosrev.2021.100413
Jun, S., & Yi, J. (2020). What Makes Followers Loyal? The Role of Influencer Interactivity in Building Influencer Brand Equity. Journal of Product & Brand Management, 29(6), 803–814. https://doi.org/10.1108/JPBM-02-2019-2280
Kim, A. J., & Johnson, K. K. P. (2016). Power of Consumers Using Social Media: Examining the Influences of Brand-related User-generated Content on Facebook. Computers in Human Behavior, 58, 98–108. https://doi.org/10.1016/j.chb.2015.12.047
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.3115/v1/D14-1181
Lambert-Pandraud, R., & Laurent, G. (2010). Why do Older Consumers Buy Older Brands? The Role of Attachment and Declining Innovativeness. Journal of Marketing, 74(5), 104–121. https://doi.org/10.1509/jmkg.74.5.104
Laroche, M., Habibi, M. R., & Richard, M.-O. (2013). To be or Not to be in Social Media: How Brand Loyalty is Affected by Social Media? International Journal of Information Management, 33(1), 76–82. https://doi.org/10.1016/j.ijinfomgt.2012.07.003
Lee, M. K. O., Shi, N., Cheung, C. M. K., Lim, K. H., & Sia, C. L. (2011). Consumer’s Decision to Shop Online: The Moderating Role of Positive Informational Social Influence. Information & Management, 48(6), 185–191. https://doi.org/10.1016/j.im.2010.08.005
Leyrer-Jackson, J. M., & Wilson, A. K. (2018). The Associations Between Social-media Use and Academic Performance among Undergraduate Students in Biology. Journal of Biological Education, 52(2), 221–230. https://doi.org/10.1080/00219266.2017.1307246
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized Bert Pretraining Approach. arXiv Preprint arXiv:1907.11692.
Loureiro, D., Barbieri, F., Neves, L., Anke, L. E., & Camacho-Collados, J. (2022). TimeLMs: Diachronic Language Models From Twitter. arXiv Preprint arXiv:2202.03829.
Martin-Domingo, L., Martín, J. C., & Mandsberg, G. (2019). Social Media as a Resource for Sentiment Analysis of Airport Service Quality (ASQ). Journal of Air Transport Management, 78, 106–115. https://doi.org/10.1016/j.jairtraman.2019.01.004
Nandwani, P., & Verma, R. (2021). A Review on Sentiment Analysis and Emotion Detection from Text. Social Network Analysis and Mining, 11(1), 81. https://doi.org/10.1007/s13278-021-00776-6
Nyadzayo, M. W., Johnson, L. W., & Rossi, M. (2020). Drivers and Outcomes of Brand Engagement in Self-concept for Luxury Fashion Brands. Journal of Fashion Marketing and Management: An International Journal, 24(4), 589–609. https://doi.org/10.1108/JFMM-05-2018-0070
Obar, J. A., & Wildman, S. (2015). Social Media Definition and the Governance Challenge: An Introduction to the Special Issue. SPECIAL ISSUE ON THE GOVERNANCE OF SOCIAL MEDIA, 39(9), 745–750. https://doi.org/10.1016/j.telpol.2015.07.014
Park, H., & Kim, Y.-K. (2014). The Role of Social Network Websites in the Consumer–Brand Relationship. Journal of Retailing and Consumer Services, 21(4), 460–467. https://doi.org/10.1016/j.jretconser.2014.03.011
Pavlicek, A., Potančok, M., & Čermák, R. (2020). Information and Process Management of Successful YouTube Channels: The Influence of the Frequency of Uploads on the Number of Subscribers. 2020 International Conference on Engineering Management of Communication and Technology (EMCTECH), 1–7.
Peng, C., & Kim, Y. G. (2014). Application of the Stimuli-Organism-Response (S-O-R) Framework to Online Shopping Behavior. Journal of Internet Commerce, 13(3–4), 159–176. https://doi.org/10.1080/15332861.2014.944437
Qin, L., Kim, Y., Hsu, J., & Tan, X. (2011). The Effects of Social Influence on User Acceptance of Online Social Networks. International Journal of Human–Computer Interaction, 27(9), 885–899. https://doi.org/10.1080/10447318.2011.555311
Rajendran, P. T., Creusy, K., & Garnes, V. (2024). Shorts on the Rise: Assessing the Effects of YouTube Shorts on Long-Form Video Content.
Rawashdeh, M., Alhamid, M. F., Alja’am, J. M., Alnusair, A., & El Saddik, A. (2016). Tag-based Personalized Recommendation in Social Media Services. Multimedia Tools and Applications, 75(21), 13299–13315. https://doi.org/10.1007/s11042-015-2813-0
Ribeiro, M. H., & West, R. (2021). Youniverse: Large-scale Channel and Video Metadata from English-speaking Youtube. Proceedings of the International AAAI Conference on Web and Social Media, 15, 1016–1024.
Risselada, H., de Vries, L., & Verstappen, M. (2018). The Impact of Social Influence on the Perceived Helpfulness of Online Consumer Reviews. European Journal of Marketing, 52(3/4), 619–636. https://doi.org/10.1108/EJM-09-2016-0522
Schwemmer, C., & Ziewiecki, S. (2018). Social Media Sellout: The Increasing Role of Product Promotion on YouTube. Social Media+ Society, 4(3), 2056305118786720.
Steinert, S. (2021). Corona and Value Change. The Role of Social Media and Emotional Contagio. Ethics and Information Technology, 23(1), 59–68. https://doi.org/10.1007/s10676-020-09545-z
Tahayna, B. M., Ayyasamy, R. K., & Akbar, R. (2022). Automatic Sentiment Annotation of Idiomatic Expressions for Sentiment Analysis Task. IEEE Access, 10, 122234–122242.
Tobon, S., & García-Madariaga, J. (2021). The Influence of Opinion Leaders’ eWOM on Online Consumer Decisions: A Study on Social Influence. Journal of Theoretical and Applied Electronic Commerce Research, 16(4), 748–767. https://doi.org/10.3390/jtaer16040043
Torres Hortelano, L. J. (2019). Audio-Visual Genres and Polymediation in Successful Spanish YouTubers. Future Internet, 11(2), 40.
Umer, N. M. (2023). Twitter Sentiment Analysis of IKEA [PhD Thesis]. Yeditepe University.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need.
Violot, C., Elmas, T., Bilogrevic, I., & Humbert, M. (2024, May). Shorts vs. Regular Videos on YouTube: A Comparative Analysis of User Engagement and Content Creation Trends. ACM Web Science Conference. https://doi.org/10.1145/3614419.3644023
Wadera, D., & Sharma, V. (2019). Impulsive Buying Behavior in Online Fashion Apparel Shopping: An Investigation of the Influence of the Internal and External Factors among Indian Shoppers.
Wang, R., & Chan-Olmsted, S. (2020). Content Marketing Strategy of Branded YouTube Channels. Journal of Media Business Studies, 17(3–4), 294–316. https://doi.org/10.1080/16522354.2020.1783130
Wang, Y., Huang, M., Zhu, X., & Zhao, L. (2016). Attention-based LSTM for Aspect-level Sentiment Classification (p. 615). https://doi.org/10.18653/v1/D16-1058
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A Survey on Sentiment Analysis Methods, Applications, and Challenges. Artificial Intelligence Review, 55(7), 5731–5780. https://doi.org/10.1007/s10462-022-10144-1
Wijaya, A., & Abdullah, S. (2022). Modern Modern Community Capital Social (Case Study on Increasing the Popularity of Millennial Youtubers in Makassar City). LEGAL BRIEF, 11(2), 489–496.
Wijaya, A., Abdullah, S., & Muhammad, R. (2021). Digital Community Social Capital: Case Study on Increasing the Popularity of Millennial Youtubers in Makassar City. Journal of Asian Multicultural Research for Social Sciences Study, 2(2), 70–80.
Wisankosol, P. (2021). YouTube Secrets: Critical Success Factors for Youtubers in Thailand. Journal of Global Business Review., 23(1), 76–90.
Zhao, K., Stylianou, A. C., & Zheng, Y. (2018). Sources and Impacts of Social Influence from Online Anonymous User Reviews. Information & Management, 55(1), 16–30. https://doi.org/10.1016/j.im.2017.03.006
Zhou, S., & Guo, B. (2017). The Order Effect on Online Review Helpfulness: A Social Influence Perspective. Decision Support Systems, 93, 77–87. https://doi.org/10.1016/j.dss.2016.09.016
Zhou, T. (2011). Understanding Online Community User Participation: A Social Influence Perspective. Internet Research, 21(1), 67–81. https://doi.org/10.1108/10662241111104884
Kemp, S. (2023). DIGITAL 2023: GLOBAL DIGITAL OVERVIEW.
https://datareportal.com/reports/digital-2023-global-digital-overview
指導教授 曾筱珽(Hsiao-Ting Tseng) 審核日期 2024-8-22
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明