The marketing strategy of online video based on danmaku-video: A bimodal analysis

doi:10.3724/SP.J.1042.2021.01561

Abstract

Abstract:

As the rapid development of Internet, danmaku video emerges. This user-video interactive mode has new features, such as real-time dynamic emotion expression and multimodal emotion distribution. Meanwhile, the new features also bring challenges to practical research work, for instance, user portrait depiction is more difficult, video recommendation and advertising need to be more accurate. Existing research has not yet deeply analyzed new features of danmaku video, nor fully explored the academic research value for itself. Based on the theories of psychology, marketing, and some other interdisciplinary frontier knowledge, as well as combined with deep learning, natural language processing technology and system dynamics method, this study attempts to analyze and model danmaku video data from a data-driven perspective, for deeply mining the potential business value of video big data. The objective of the study is to create a high-quality online video intelligent marketing platform. A sentimental recognition approach is proposed to capture the internal relationship between sentimental features of danmaku text and visual sentimental features of video, so as to represent users’ sentiment. Considering the influence of users’ dynamic emotion on behavior, the dynamic user portrait with sentimental features is constructed to describe full picture of user characteristics, as well as to explore the access logic and behavioral preferences that are hidden behind behavior data. By positioning user roles, the sticky marketing mechanism of online video platform is established. Furthermore, focused on different advertisement types, like creative ads, interstitial ads and oral ads, this study explores the impact of video ads on the emotional and psychological changes of users, reveals the correlation between users’ sentiment changes and video ads insertion mode, and thus puts forward the “content customized” dynamic advertising strategy for danmaku video. It not only enriches the existing research, but also provides theoretical guidance and decision-making support for online video platforms to accurately locate and analyze user demands. In particularly, the contributions of the study include: (1) Previous research on marketing strategy of online video platform mainly focused on platform pricing, brand reputation and user satisfaction, ignoring external stimulus factors, such as dynamic factors of user behavior and emotional changes caused by danmaku video advertising. This paper combines big data of danmaku video to analyze users’ emotional responses to the external stimuli. Based on multimodal attention fusion, the hierarchical deep association co-attention model is designed to achieve users’ sentiment recognition. The paper reveals the correlation between video ads and users’ emotional changes, proposes the dynamic intercutting strategy of customized video ads, innovates the video advertising marketing model, and helps advertisers to enhance the brand marketing value; (2) The traditional method of constructing user portrait does not focus on the strong correlation among users’ sentiment, natural attribute and behavior. To this end, the study is driven by the sticky marketing of online video platform. Based on three dimensions of users’ sentiment, natural attribute and behavior, a dynamic user portrait model with four modules of basic data, behavior modeling, service application and evaluation feedback is established. Considering user demand and dynamic user portrait, the model of sticky marketing mechanism is constructed to recommend rich content options for users, to increase users’ stickiness and to prolong users’ usage time in online video platform. Thus, it forms a benign marketing mode of flow closed loop and completes the flow transformation between user value and content value in online video platform. Through this study, user needs will be met from a higher quality level, the accuracy of video advertising will be comprehensively improved, and the marketing value can be maximized in online video platform.

Key words: danmaku video, bimodal sentiment fusion, dynamic user profile, sticky marketing mechanism, dynamic advertising strategy

CLC Number:

B849: F273

LI Zhi, ZHU Chunhong. The marketing strategy of online video based on danmaku-video: A bimodal analysis[J]. Advances in Psychological Science, 2021, 29(9): 1561-1575.

Figures/Tables 10

References 71

[1]	陈丹, 罗烨, 吴智勤. (2019). 基于大数据挖掘和用户画像的高校图书馆个性化服务研究. 图书馆研究与工作, 4, 50-59.
[2]	邓扬, 张晨曦, 李江峰. (2017). 基于弹幕情感分析的视频片段推荐模型. 计算机应用, 37(4), 1065-1070.
[3]	范钧, 潘健军. (2016). 剧情式视频广告中品牌—情节信息对受众传播意愿的影响. 营销科学学报, 12(2), 111-123.
[4]	高茂庭, 杨涛. (2020). 融合协同过滤和主题模型的弹幕视频推荐算法. 计算机应用研究, 37(12), 3565-3568+ 3577.
[5]	龚诗阳, 李倩, 余承锬. (2017). 在线社交对消费者需求的影响研究—基于网络视频产业的实证分析. 中国软科学, 7, 39-48.
[6]	郭伏, 叶国全, 李明明, 吕伟. (2017). 植入式广告显著度对广告效果影响的眼动研究—以电影植入式广告为例. 营销科学学报, 4, 18-33.
[7]	洪庆, 王思尧, 赵钦佩, 李江峰, 饶卫雄. (2018). 基于弹幕情感分析和聚类算法的视频用户群体分类. 计算机工程与科学, 40(6), 1125-1139.
[8]	勒伯夫 G.(2012). 粘性营销: 新时代的营销圣经(派力译译). 北京: 中国商业出版社.
[9]	李泽中, 张海涛, 张鑫蕊, 王兴鲁, 孙鸿飞. (2020). 融合用户社交与情境信息的虚拟知识社区个性化知识推荐研究. 情报理论与实践, 43(4), 152-158
[10]	刘海鸥, 刘旭, 姚苏梅. 谢姝琳. (2019). 基于舆情画像的在线社交用户信息传播特征统计分析. 现代情报, 39(9):64-73.
[11]	刘启元, 张栋, 吴良庆, 李寿山. (2019). 基于上下文增强LSTM的多模态情感分析. 计算机科学, 46(11), 181-185.
[12]	刘婷艳, 王晰巍, 贾若男, 王微. (2020). 视频网站用户生成内容国内外发展动态及发展趋势. 情报科学, 38(10), 133-140.
[13]	王敏, 徐健. (2019). 视频弹幕与字幕的情感分析与比较研究. 图书情报知识, 5, 109-119.
[14]	王贇芝, 王雪, 查先进. (2020). 弹幕视频网站用户从众信息评论行为驱动因素探索. 信息资源管理学报, 10(4), 60-69.
[15]	吴剑云, 胥明珠. (2021). 基于用户画像和视频兴趣标签的个性化推荐. 情报科学, 39(1), 128-134.
[16]	徐海玲, 张海涛, 魏明珠, 尹慧子. (2019). 社交媒体用户画像的构建及资源聚合模型研究. 图书情报工作, 63(9), 109-115.
[17]	俞淑平, 陈刚. (2011). 一种高效的行为定向广告投放算法. 计算机应用与软件, 28(1), 4-7.
[18]	袁海霞, 方青青, 白琳. (2020). 弹幕对在线消费行为过程影响的时变效应研究. 管理学报, 17(7), 1059-1066.
[19]	张海涛, 崔阳, 王丹, 宋拓. (2018). 基于概念格的在线健康社区用户画像研究. 情报学报, 37(9), 912-922.
[20]	张帅, 王文韬, 周华任, 占南. (2018). 基于扎根理论的弹幕视频网站用户使用行为驱动因素研究. 情报理论与实践, 41(7), 117-122.
[21]	张涛, 翁康年, 邓悦, 杨满, 张玥杰. (2021). 基于网络浏览行为的小众领域用户画像建模. 系统工程理论与实践, 40(3), 641-652.
[22]	庄须强, 刘方爱. (2018). 基于AT-LSTM的弹幕评论情感分析. 数字技术与应用, 36(2), 210-212.
[23]	Bansal T., Das M., & Bhattacharyya C. (2015). Content driven user profiling for comment-worthy recommendations of news and blog articles. ACM Conference on Recommender Systems, 195-202.
[24]	Bao L. N., Le D.-N., van Chung L., & Nguyen G. N.(2016). Performance evaluation of video-based face recognition approaches for online video contextual advertisement user-oriented system. Proceedings paper: Information Systems Design and Intelligent Applications Conference, 3, 287-295.
[25]	Barnett S. B., & Cerf M. (2017). A ticket for your thoughts: Method for predicting content recall and sales using neural similarity of moviegoers. Journal of Consumer Research, 44(1):160-181.
[26]	Belanche D., Flavián C., & Pérez-Rueda A. (2017). Understanding interactive online advertising: congruence and product involvement in highly and lowly arousing, skippable video ads. Journal of Interactive Marketing, 37, 75-88. doi: 10.1016/j.intmar.2016.06.004 URL
[27]	Bellman S., Nenycz-Thiel M., Kennedy R., Larguinat L., McColl B., & Varan D. (2017). What makes a television commercial sell? Using biometrics to identify successful ads. Journal of Advertising Research, 57(1), 53-66. doi: 10.2501/JAR-2016-051 URL
[28]	Bendak S., & Al-Saleh K. (2010). The role of roadside advertising signs in distracting drivers. International Journal of Industrial Ergonomics, 40(3), 233-236. doi: 10.1016/j.ergon.2009.12.001 URL
[29]	Berlyne D. E. (1970). Novelty, complexity, and hedonic value. Perception & Psychophysics, 8, 279-286. doi: 10.3758/BF03212593 URL
[30]	Brehm J. W. A. (1966). A theory of psychological reactance. New York: Academic Press Inc.
[31]	Bruce N. I., BeckerM, & Reinartz W. (2020). Communicating brands in television advertising. Journal of Marketing Research, 57(2), 236-256. doi: 10.1177/0022243719892576 URL
[32]	Chen X., Nguyen T. V., Shen Z. Q., & Kankanhalli M. (2019). Livesense:Contextual advertising in live streaming videos. Proceedings paper of 27th ACM International Conference on Multimedia (MM), 19, 392-400.
[33]	Cheng Z. -Q., Liu Y., Wu X., & Hua X. -S. (2016). Video ecommerce: Towards online video advertising. Proceedings of the 24^th ACM Multimedia Conference, 16, 1365-1374.
[34]	Chiang H. S., & Hsiao K. L. (2015). Youtube stickiness: The needs, personal, and environmental perspective. Internet Research, 25(1), 85-106. doi: 10.1108/IntR-11-2013-0236 URL
[35]	CostaM, BonettiL, Vignali V., Bichicchi A., Lantieri C., & Simone A. (2019). Driver's visual attention to different categories of roadside advertising signs. Applied Ergonomics, 78, 127-136. doi: 10.1016/j.apergo.2019.03.001 URL
[36]	Fossen B. L., & Schweidel D. A. (2017). Television advertising and online word-of-mouth: An empirical investigation of social TV activity. Marketing Science, 36(1), 105-123. doi: 10.1287/mksc.2016.1002 URL
[37]	Gorodnichenko Y., & Weber M. (2016). Are sticky prices costly? Evidence from the stock market. American Economic Review, 106(1), 165-199.
[38]	He M., Ge Y., WuL. Chen E., & Tan C. (2016). Predicting the popularity of Dan Mu-enabled videos: A multi-factor view. Database Systems for Advanced Applications, 351-366.
[39]	Hu A., & Flaxman S. (2018). Multimodal sentiment analysis to explore the structure of emotions. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, 350-358.
[40]	Huang F., Zhang X., Zhao Z., Xu J., & Li Z. (2019). Image-text sentiment analysis via deep multimodal attentive fusion. Knowledge-Based Systems, 167, 26-37. doi: 10.1016/j.knosys.2019.01.019 URL
[41]	Hui S. K., Meyvis T., & Assael H. (2014). Analyzing moment-to-moment data using a bayesian functional linear model: Application to TV show pilot testing. Marketing Science, 33(2), 222-240. doi: 10.1287/mksc.2013.0835 URL
[42]	Hogarth R. M., & Einhorn H. J. (1992). Order effects in belief updating the belief adjustment model. Cognitive Psychology, 24(1), 1-55. doi: 10.1016/0010-0285(92)90002-J URL
[43]	Kim H., & Bruce N. I. (2018). Should sequels differ from original movies in pre-launch advertising schedule? Lessons from consumers’ online search activity. International Journal of Research in Marketing, 35(1), 116-143. doi: 10.1016/j.ijresmar.2017.12.006 URL
[44]	Kumar A., Srinivasan K., Cheng W. -H., & Zomaya A. Y. (2020). Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Information Processing & Management, 57(1), 1-25.
[45]	Lau R. Y. K., Zhang W., & Xu W. (2018). Parallel aspect-oriented sentiment analysis for sales forecasting with big data. Production and Operations Management, 27(10), 1775-1794. doi: 10.1111/poms.2018.27.issue-10 URL
[46]	Li D. F., Hu B. T., Chen Q. C., Wang X., Qi Q., Wang L., & Liu H. (2021). Attentive capsule network for click-through rate and conversion rate prediction in online advertising. Knowledge-Based Systems, 211, 106522. doi: 10.1016/j.knosys.2020.106522 URL
[47]	Li G., Park S. T., & Jin H. (2017). Antecedents of WeChat group chatting user’s stickiness and organizational commitment. Journal of Engineering and Applied Sciences, 12(1), 5708-5713.
[48]	Li Z., Li R., & Jin G. (2020). Sentiment analysis of danmaku videos based on naïve bayes and sentiment dictionary. IEEE Access, 5, 75073-75084.
[49]	Liu D. -., Liao Y. -., Chung Y. -., & Chen K. -Y. (2019). Advertisement recommendation based on personal interests and ad push fairness. Kybernetes, 48(8), 1-21.
[50]	Lu H. -P., & Lee M. -R. (2010). Demographic differences and the antecedents of blog stickiness. Online Information Review, 34 (1), 21-38. doi: 10.1108/14684521011024100 URL
[51]	Lv G., Xu T., Chen E., Liu Q., & Zheng Y. (2016). Reading the videos:temporal labeling for crowdsourced time-sync videos based on semantic embedding. Thirtieth AAAI Conference on Artificial Intelligence, 3000-3006.
[52]	Majumder N., Hazarika D., Gelbukh A., Cambria E., & Poria S. (2018). Multimodal sentiment analysis using hierarchical fusion with context modeling. Knowledge- Based Systems, 161, 124-133. doi: 10.1016/j.knosys.2018.07.041 URL
[53]	Olbrich R., & Holsing C. (2011). Modeling consumer purchasing behavior in social shopping communities with clickstream data. International Journal of Electronic Commerce, 16(2), 15-40. doi: 10.2753/JEC1086-4415160202 URL
[54]	Peng D., Tian W., Zhu M., Ren Y., Lin X., & Li M. (2020). TargetingVis: Visual exploration and analysis of targeted advertising data. Journal of Visualization, 23(6), 1113-1127. doi: 10.1007/s12650-020-00671-w URL
[55]	Piao G., & Breslin J. G. (2016). Exploring dynamics and semantics of user interests for user modeling on Twitter for link recommendations. ACM International conference on semantic systems, 81-88.
[56]	Ramanathan S., & McGill A. L. (2007). Consuming with others: Social influences on moment-to-moment and retrospective evaluations of an experience. Journal of Consumer Research, 34(4), 506-524. doi: 10.1086/520074 URL
[57]	Rong K., Xiao F., Zhang X., & Wang J. (2019). Platform strategies and user stickiness in the online video industry. Technological Forecasting & Social Change, 143, 249-259.
[58]	,, & Sánchez P., & Bellogín A.(2019). Building user profiles based on sequences for content and collaborative filtering. Information Processing & Management, 56(1), 192-211. doi: 10.1016/j.ipm.2018.10.003 URL
[59]	Simmonds L., Bellman S., Kennedy R., Nenycz-Thiel M., & Bogomolova S. (2020). Moderating effects of prior brand usage on visual attention to video advertising and recall: An eye-tracking investigation. Journal of Business Research, 111, 241-248. doi: 10.1016/j.jbusres.2019.02.062 URL
[60]	Skehan P. (1996). A Framework for the Implementation of task-based instruction. Applied Linguistics, 17(1), 38-62. doi: 10.1093/applin/17.1.38 URL
[61]	Sun Q., Niu J., Yao Z., & Yan H. (2019). Exploring eWOM in online customer reviews: Sentiment analysis at a fine-grained level. Engineering Applications of Artificial Intelligence, 81, 68-78. doi: 10.1016/j.engappai.2019.02.004 URL
[62]	Teixeira T. S., Wedel M., & Pieters R. (2010). Moment- to-moment optimal branding in TV commercials: Preventing avoidance by pulsing. Marketing Science, 29(5), 783-804. doi: 10.1287/mksc.1100.0567 URL
[63]	Thompson J., Leung B., Blair M. R., & Taboada M. (2017). Sentiment analysis of player chat messaging in the video game StarCraft 2: Extending a lexicon-based model. Knowledge-Based Systems, 137, 149-162. doi: 10.1016/j.knosys.2017.09.022 URL
[64]	Travis D. (2003). E-commerce usability: Tools and techniques to perfect the on-line experience. CRC Press, London.
[65]	Trusov M., Ma L., & Jamal Z. (2016). Crumbs of the cookie: User profiling in customer-base analysis and behavioral targeting. Marketing Science, 35(3), 405-426. doi: 10.1287/mksc.2015.0956 URL
[66]	Wang G., Zhuo L., Li J., Ren D., & Zhang J. (2018). An efficient method of content-targeted online video advertising. Journal of Visual Communication and Image Representation, 50, 40-48. doi: 10.1016/j.jvcir.2017.11.001 URL
[67]	Young C., Gillespie B., & Otto C. (2019). The impact of rational, emotional, and physiological advertising images on purchase intention: how TV ads influence brand memory. Journal of Advertising Research, 59(3), 329-341. doi: 10.2501/JAR-2019-010 URL
[68]	Yu S., Christakopoulou E., & Gupta A. (2016). Identifying decision makers from professional social networks. ACM SIGKDD International Conference on Knowledge Discovery and Data mining, New York: ACM, 333-342.
[69]	Zhang Q., Wang W., & Chen Y. (2020). Frontiers: In-consumption social listening with moment-to-moment unstructured data: The case of movie appreciation and live comments. Marketing Science, 39(2), 1-11. doi: 10.1287/mksc.2019.1205 URL
[70]	Zhao F., Zhu Y., Jin H., & Yang L. T. (2016). A personalized hashtag recommendation approach using LDA-based topic model in microblog environment. Future Generation Computer Systems, 65, 196-206. doi: 10.1016/j.future.2015.10.012 URL
[71]	Zhou J. L., Zhou J., Ding Y., & Wang H. (2019). The magic of danmaku: A social interaction perspective of gift sending on live streaming platforms. Electronic Commerce Research and Applications, 34, 1-9.

主题	节目举例	广告类型
综艺	奇葩说第六集(爱奇艺)	口播广告、视频浮层广告
	演员请就位(腾讯)	口播广告、视频浮层广告、创意中插广告、中播广告
	明星大侦探5(芒果TV)	口播广告、创意中插广告、视频浮层广告
电视剧	小欢喜(爱奇艺)	创意中插广告、中播广告、视频浮层广告
	三生三世十里桃花(腾讯)	中播广告、创意中插广告
	海棠经雨胭脂透(芒果TV)	中播广告、视频浮层广告
电影	狙击手(爱奇艺)	中播广告
电影	中国机长(腾讯)	中播广告
动漫	海贼王(爱奇艺)	中播广告、视频浮层广告
	秦时明月之君临天下(优酷)	中播广告、视频浮层广告、创意中插广告
	名侦探柯南(芒果TV)	视频浮层广告
体育	2019ATP男单总决赛—蒂姆VS贝雷蒂尼(爱奇艺)	中播广告、口播广告
	NBA凯尔特人VS勇士(腾讯)	中播广告
	CBA 辽宁本钢VS八一(优酷)	视频浮层广告

主题	节目举例	广告类型
综艺	奇葩说第六集(爱奇艺)	口播广告、视频浮层广告
	演员请就位(腾讯)	口播广告、视频浮层广告、创意中插广告、中播广告
	明星大侦探5(芒果TV)	口播广告、创意中插广告、视频浮层广告
电视剧	小欢喜(爱奇艺)	创意中插广告、中播广告、视频浮层广告
	三生三世十里桃花(腾讯)	中播广告、创意中插广告
	海棠经雨胭脂透(芒果TV)	中播广告、视频浮层广告
电影	狙击手(爱奇艺)	中播广告
电影	中国机长(腾讯)	中播广告
动漫	海贼王(爱奇艺)	中播广告、视频浮层广告
	秦时明月之君临天下(优酷)	中播广告、视频浮层广告、创意中插广告
	名侦探柯南(芒果TV)	视频浮层广告
体育	2019ATP男单总决赛—蒂姆VS贝雷蒂尼(爱奇艺)	中播广告、口播广告
	NBA凯尔特人VS勇士(腾讯)	中播广告
	CBA 辽宁本钢VS八一(优酷)	视频浮层广告