Deep Learning Model to Analyze Customer’s Satisfaction
Bouzakraoui Moulay Smail1, Sadiq Abdelalim2, Youssfi Alaoui Abdessamad3
1Bouzakraoui Moulay Smail*, SIM team of MISC LaboratoryFaculty of Science, University IBN TOFAIL, Kenitra, Morocco
2Sadiq Abdelalim, SIM team of MISC LaboratoryFaculty of Science, University IBN TOFAIL, Kenitra, Morocco
3Youssfi Alaoui Abdessamad, IRDA Team, ADMIR Laboratory, Rabat IT Center, ENSIAS, Mohammed V University, Rabat, Morocco.
Manuscript received on March 28, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 1709-1714 | Volume-9 Issue-4, April 2020. | Retrieval Number: C6610029320/2020©BEIESP | DOI: 10.35940/ijeat.C6610.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, measuring customer satisfaction is an important strategic tool for companies; many manual methods exist to measure customer’s satisfaction. However, the results have not effective and efficient. In this paper, we propose a new method for facial emotion detection to recognize customer’s satisfaction using a deep learning model. We used a convolutional neural network to detect facial key points. These key points help us to extract geometric features from customer’s emotional faces. Indeed, we computed distances between neutral face and negative or positive feedback. After that, we classified these distances by using Support Vector Machine (SVM), KNN, Random Forest, and Decision Tree. To evaluate the performance of our approach, we tested our algorithm by using FACEDB and JAFFE datasets. We found that SVM is the most performant classifier. We obtained 96% as accuracy by using FACEDB dataset and 95% by using JAFFE dataset.
Keywords: Emotion recognition, Customer satisfaction, SVM, KNN, Decision Tree.