Open AccessDeep Learning Model to Analyze Customer’s Satisfaction
Author(s) -
Bouzakraoui Moulay Smail,
Abdelalim Sadiq,
Youssfi Alaoui Abdessamad
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c6610.049420
Subject(s) - computer science , support vector machine , customer satisfaction , artificial intelligence , random forest , decision tree , machine learning , key (lock) , convolutional neural network , classifier (uml) , deep learning , artificial neural network , face (sociological concept) , pattern recognition (psychology) , data mining , marketing , social science , computer security , sociology , business
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.