[Objective] This paper uses neural networks to detect facial expressions in real-time video streams, aiming to explore the correlation between CEO’s emotional characteristics at product launch events and media attention. [Methods] A total of 566 product launch event videos from 34 electronics companies were collected. Facial expressions of CEOs during the events were detected using models like MTCNN. Then, we investigated the patterns of CEO’s emotional expressions and explored the influence of their characteristics on media attention with correlation analysis. [Results] CEOs of different companies exhibited distinct emotional expression patterns during the launch events, which could be clustered closely associated with the main product types of the companies. Each cluster also had significant emotional inertia expression and influence trends. The proportion of anger was positively correlated with media attention during the launch events at a confidence level of 95% (with Pearson’s correlation coefficients exceeding 0.21). [Limitations] This study focuses on electronic product launch events, and the collected data from various companies were not unevenly distributed. [Conclusions] Deep learning enable the rapid detection of CEO facial expressions based on video streams. This study analyzed CEO’s emotional expression patterns and their influence and provided suggestions for CEO’s emotional management in brand communication.
李洋, 赵吉昌. 基于神经网络的CEO表情分析及其对发布会媒体关注度的影响*[J]. 数据分析与知识发现, 2023, 7(8): 46-61.
Li Yang, Zhao Jichang. CEO Facial Expression Analysis Based on Neural Networks and Its Impacts on Media Attention at Press Conferences. Data Analysis and Knowledge Discovery, 2023, 7(8): 46-61.
(Li Feng, Cui Kangle. Research on the Impact of Corporate Social Responsibility Behavior on Consumers in the Host Country: From the Perspective of Chinese Brand Internationalization[J]. East China Economic Management, 2022, 36(6): 89-104.)
[3]
张彩燕. 企业社会责任对企业品牌的影响作用研究[J]. 现代商业, 2022(5): 24-26.
[3]
(Zhang Caiyan. Research on the Influence of Corporate Social Responsibility on Corporate Brand[J]. Modern Business, 2022(5): 24-26.)
(Haroon Hakimi. The Effective Elements of a Press Conference:The Coorientation Approach of Public Relations Practitioner and Journalists[D]. Chongqing: Southwest University, 2021.)
[7]
姜继涛. 产品发布会在手机营销中的作用与对策分析[J]. 现代营销, 2018(1): 67.
[7]
(Jiang Jitao. Analysis of the Function and Countermeasures of Product Launch in Mobile Phone Marketing[J]. Modern Marketing, 2018(1): 67.)
[8]
Li S, Deng W H. Deep Facial Expression Recognition: A Survey[J]. IEEE Transactions on Affective Computing, 2022, 13(3): 1195-1215.
doi: 10.1109/TAFFC.2020.2981446
[9]
Donahue J, Jia Y Q, Vinyals O. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition[C]// Proceedings of the 31st International Conference on Machine Learning - Volume 32. 2014: I-647-I-655.
[10]
Jia Y Q, Shelhamer E, Donahue J, et al. Caffe: Convolutional Architecture for Fast Feature Embedding[C]// Proceedings of the 22nd ACM International Conference on Multimedia. 2014: 675-678.
[11]
Yu Z D, Zhang C. Image Based Static Facial Expression Recognition with Multiple Deep Network Learning[C]// Proceedings of the 2015 ACM on International Conference on Multimodal Interaction. 2015: 435-442.
[12]
Schroff F, Kalenichenko D, Philbin J. FaceNet: A Unified Embedding for Face Recognition and Clustering[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. 2015: 815-823.
[13]
Ekman P, Friesen W V. Constants Across Cultures in the Face and Emotion[J]. Journal of Personality and Social Psychology, 1971, 17(2): 124-129.
doi: 10.1037/h0030377
pmid: 5542557
[14]
Ben X Y, Ren Y, Zhang J P, et al. Video-Based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(9): 5826-5846.
[15]
Liu Y J, Zhang J K, Yan W J, et al. A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition[J]. IEEE Transactions on Affective Computing, 2016, 7(4): 299-310.
doi: 10.1109/T-AFFC.5165369
[16]
Kim D H, Baddar W J, Ro Y M. Micro-Expression Recognition with Expression-State Constrained Spatio-Temporal Feature Representations[C]// Proceedings of the 24th ACM International Conference on Multimedia. 2016: 382-386.
[17]
Zhang K H, Huang Y Z, Du Y, et al. Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks[J]. IEEE Transactions on Image Processing, 2017, 26(9): 4193-4203.
doi: 10.1109/TIP.2017.2689999
pmid: 28371777
[18]
Zhou E J, Cao Z M, Yin Q. Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not [OL]. arXiv Preprint, arXiv: 1501.04690.
[19]
Fan H Q, Cao Z M, Jiang Y N, et al. Learning Deep Face Representation[OL]. arXiv Preprint, arXiv: 1403.2802.
(Wang Linlin. An Empirical Study on the Influence of CEO’s Emotional Characteristics on Corporate Strategic Decision-Making[D]. Hefei: University of Science and Technology of China, 2018.)
[21]
Mesquita B, Boiger M. Emotions in Context: A Sociodynamic Model of Emotions[J]. Emotion Review, 2014, 6(4): 298-302.
doi: 10.1177/1754073914534480
[22]
LaBar K S, Cabeza R. Cognitive Neuroscience of Emotional Memory[J]. Nature Reviews Neuroscience, 2006, 7: 54-64.
doi: 10.1038/nrn1825
pmid: 16371950
[23]
Kuppens P, Verduyn P. Emotion Dynamics[J]. Current Opinion in Psychology, 2017, 17: 22-26.
doi: S2352-250X(16)30201-9
pmid: 28950968
[24]
Kuppens P, Verduyn P. Looking at Emotion Regulation Through the Window of Emotion Dynamics[J]. Psychological Inquiry, 2015, 26(1): 72-79.
doi: 10.1080/1047840X.2015.960505
[25]
Larsen R J. The Stability of Mood Variability: A Spectral Analytic Approach to Daily Mood Assessments[J]. Journal of Personality and Social Psychology, 1987, 52(6): 1195-1204.
doi: 10.1037/0022-3514.52.6.1195
(Yu Ningli, Yi Dongyun, Tu Xianqin. Analysis of Autocorrelation and Partial Correlation Functions in Time Series[J]. Mathematical Theory and Applications, 2007, 27(1): 54-57.)
[27]
Bertrand M, Schoar A. Managing with Style: The Effect of Managers on Firm Policies[J]. The Quarterly Journal of Economics, 2003, 118(4): 1169-1208.
doi: 10.1162/003355303322552775
[28]
Gomulya D, Wong E M, Ormiston M E, et al. The Role of Facial Appearance on CEO Selection after Firm Misconduct[J]. Journal of Applied Psychology, 2017, 102(4): 617-635.
doi: 10.1037/apl0000172
pmid: 27991800
[29]
Sun Y J, Akansu A A, Cicon J E. The Power of Fear: Facial Emotion Analysis of CEOs to Forecast Firm Performance[C]// Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration. 2014: 695-702.
(Tian Min, Li Chunqing, Xiao Qinglong. The Impact of Corporate Social Responsibility Activities on Brand Evaluation[J]. Nankai Business Review, 2014, 17(6): 19-29.)
(Pei Rongkang. The Influence of Enterprise Weibo Content on Online Word of Mouth and Brand Recognition and the Countermeasures[J]. Enterprise Reform and Management, 2022(2): 114-116.)
[32]
Pe M L, Kuppens P. The Dynamic Interplay Between Emotions in Daily Life: Augmentation, Blunting, and the Role of Appraisal Overlap[J]. Emotion, 2012, 12(6): 1320-1328.
doi: 10.1037/a0028262
pmid: 22642355
[33]
Barrett L F, Gross J J, Christensen T C, et al. Knowing What You’re Feeling and Knowing What to Do about It: Mapping the Relation Between Emotion Differentiation and Emotion Regulation[J]. Cognition and Emotion, 2001, 15(6): 713-724.
doi: 10.1080/02699930143000239
[34]
Nook E C, Sasse S F, Lambert H K, et al. The Nonlinear Development of Emotion Differentiation: Granular Emotional Experience is Low in Adolescence[J]. Psychological Science, 2018, 29(8): 1346-1357.
doi: 10.1177/0956797618773357
pmid: 29878880
[35]
Sun Y, Wang X G, Tang X O. Deep Convolutional Network Cascade for Facial Point Detection[C]// Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. 2013: 3476-3483.
[36]
Fan R, Xu K, Zhao J C. Weak Ties Strengthen Anger Contagion in Social Media[OL]. arXiv Preprint, arXiv: 2005.01924.
[37]
Chuai Y W, Zhao J C. Anger Can Make Fake News Viral Online[J]. Frontiers in Physics, 2022, 10: 970174.
doi: 10.3389/fphy.2022.970174