{"title":"The troika of artificial intelligence, emotional intelligence and customer intelligence","authors":"Manish Sharma, Shikha N. Khera, P. B. Sharma","doi":"10.1504/IJSCCPS.2019.100195","DOIUrl":null,"url":null,"abstract":"Emotional intelligence is to recognise emotions and emotions can be recognised by analysing face. Face reflects emotions, and thus facial images can help to identify emotions. Emotions recognition can help in conducting qualitative market research techniques like focus groups; in-depth interviews and other which can be used to generate customer intelligence. This paper provides a cross-disciplinary view of Intelligence. This paper proposes a machine learning-based model to accomplish the task of identifying emotions from given facial images. This paper uses a public database and divides the images into four groups. The feature extraction has been done by principal component analysis and the feature selection by fisher discriminant ratio. The classification has been done by support vector machine using k cross-validation. The accuracy, specificity and sensitivity are encouraging. The average accuracy is 0.84","PeriodicalId":220482,"journal":{"name":"Int. J. Soc. Comput. Cyber Phys. Syst.","volume":"294 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Soc. Comput. Cyber Phys. Syst.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJSCCPS.2019.100195","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Emotional intelligence is to recognise emotions and emotions can be recognised by analysing face. Face reflects emotions, and thus facial images can help to identify emotions. Emotions recognition can help in conducting qualitative market research techniques like focus groups; in-depth interviews and other which can be used to generate customer intelligence. This paper provides a cross-disciplinary view of Intelligence. This paper proposes a machine learning-based model to accomplish the task of identifying emotions from given facial images. This paper uses a public database and divides the images into four groups. The feature extraction has been done by principal component analysis and the feature selection by fisher discriminant ratio. The classification has been done by support vector machine using k cross-validation. The accuracy, specificity and sensitivity are encouraging. The average accuracy is 0.84