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Developing a Web Application for Recognizing Emotions in Neuromarketing

  • Filip Filipović
  • Luka Baljak
  • Tamara Naumović
  • Aleksandra Labus
  • Zorica BogdanovićEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 167)

Abstract

The subject of this paper is the development of a web application for recognizing emotions in neuromarketing. The goal is to develop an open system that should enable identification of user emotions using a webcam when accessing web content. Recognition of user emotions will be based on computer vision algorithms and machine learning. The developed web applications will serve as the basis for the development of a system that enables personalization of Internet marketing services based on user emotions in near real time. Evaluation of the developed solution has been done through an experiment. Results show that the users’ emotions can be identified using the developed system, with a satisfactory level of precision.

Keywords

Internet marketing Neuromarketing Computer vision Machine learning Emotion recognition 

Notes

Acknowledgements

The authors are thankful to the Ministry of Education, Science and Technological Development, Grant no. 174031.

References

  1. 1.
    Arthmann, C., Li, I.-P.: The Art and Science of Marketing and Neurosciences Enabled by IoT Technologies (2017)Google Scholar
  2. 2.
    Vasiljević, T., Bogdanović, Z., Rodić, B., Naumović, T., Labus, A.: Designing IoT infrastructure for neuromarketing research. In: World Conference on Information Systems and Technologies, pp. 928–935. Springer, Cham (2019)Google Scholar
  3. 3.
    Devaney, E.: How to master personalized marketing. HubSpot (2014)Google Scholar
  4. 4.
    Krajnović, A., Šikirić, D., Jakšić, D.: Neuromarketing and Customers Free Will. University of Zadar (2012)Google Scholar
  5. 5.
    Meola, A.: What is the Internet of Things (IoT) (2016)Google Scholar
  6. 6.
    Jelić, N.: Bihevioralna ekonomija, neuroekonomija, neuromarketing. JAHR-Eur. J. Bioeth. (2014)Google Scholar
  7. 7.
    Reitsma, L.: Five neuromarketing techniques every marketer should know about, newneuromarketing. https://www.newneuromarketing.com/5-neuromarketing-techniques-every-marketershould-know-about (2017)
  8. 8.
    Dooley, R.: Facial EMG: muscles don’t lie?—neuromarketing, neuromarketing. https://www.neurosciencemarketing.com/blog/articles/facialemg.html (2011)
  9. 9.
    Facial emotion detection using AI: use-cases, paralleldots. https://blog.paralleldots.com/product/facial-emotion-detection-using-ai/
  10. 10.
    McCulloch, W.S., Pitts, W., A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol. 115–133 (1943)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kriesel, D.: A Brief Introduction to Neural Networks (2007)Google Scholar
  12. 12.
    Zerium, A.: Artificial neural networks explained, medium. https://blog.goodaudience.com/artificial-neural-networks-explained-436fcf36e75 (2018)
  13. 13.
    When to use MLP, CNN, and RNN neural networks, machine learning mastery. https://machinelearningmastery.com/when-to-use-mlp-cnn-and-rnn-neural-networks/
  14. 14.
    Mishra, M.: Convolutional neural networks, explained. Datascience. https://www.datascience.com/blog/convolutional-neural-network (2019)
  15. 15.
    Face-api.js: JavaScript Face Recognition Leveraging TensorFlow.js. https://www.infoq.com/news/2018/11/faces-api-js/
  16. 16.
  17. 17.
    Gardner, O.: Attention-driven design: 23 visual principles for designing more persuasive landing pages. Unbounce (2015)Google Scholar
  18. 18.
    El Ayadi, M., Kamel, M.S., Karray, F.: Survey on speech emotion recogntion: features, classification schemes, and databases. Pattern Recogn. 44(3), 572–587 (2011)CrossRefGoogle Scholar
  19. 19.
    Schuller, B., Batliner, A., Steidl, S., Seppi, D.: Recognising realistic emotions and affect in speech: state of the art and lessons learnt from the first challenge. Speech Commun. 53(9), 1062–1087 (2011)CrossRefGoogle Scholar
  20. 20.
    Burke, R.: Hybrid web recommender systems. The AdaptiveWeb, pp. 377–408. Springer, Berlin, Heidelberg (2007)Google Scholar
  21. 21.
    Mahmood, T., Ricci, F.: Improving recommender systems with adaptive conversational strategies. In: Cattuto, C., Ruffo, G., Menczer, F., (eds.) Hypertext, pp. 73–82. ACM (2009)Google Scholar
  22. 22.
    Lops, P., De Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends, pp. 73–105 (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Faculty of Organizational SciencesUniversity of BelgradeBelgradeSerbia

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