Abstract
Accuracy is essential in developing and researching innovative products. For example, in a live customer helpline chat, an honest dialogue between its users can be achieved by a live emotion analysis and verification to corroborate messages of both ends which ultimately nullifies the deceit made to complainers on the live chat. This emotion artificial intelligent verifier works on the concept of licensing or declining authenticity of message by comparing emotions found in text messaging and facial expressions. In this paper, we proposed an artificial intelligence-based live emotion verifier that acts as an honest arbitrator which first, recognizes live facial expressions under four labels namely, ‘Happiness’, ‘Surprise’, ‘Sadness’ and ‘Hate’ using Convolutional Neural Network (CNN) using a miniXception model. Simultaneously, it predicts the same labels of emotions in live text messages using these text classifiers—Support Vector Machine (SVM), Random Forest (RF), Naive bales (NB) and Logistic Regression (LR). We observed that among all four classifiers, SVM attained the highest accuracy for text prediction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mehrabian, A., Ferris, S.R.: Inference of attitudes from nonverbal communication in two channels. J. Consult. Psychol. 31(3), 248–252 (1967)
Balabantaray, R.C., Mohammad, M., Sharma, N.: Multi-class twitter emotion classification: a new approach. Int. J. Appl. Inf. Syst. 4(1), 48–53 (2012)
Marechal, C., Mikolajewski, D., Tyburek, K., Prokopowicz, P., Bougueroua, L., Ancourt, C., Wegrzyn-Wolska, K.: Survey on AI-Based Multimodal Methods for Emotion Detection, pp. 307–314. Springer, Berlin (2019)
Azcarate, A., Hageloh, F., Van de Sande, K., Valenti, R.: Automatic Facial Emotion Recognition, pp. 1–6. Universiteit van Amsterdam (2005)
Bindu, M.H., Gupta, P., Tiwary, U.S.: Cognitive model-based emotion recognition from facial expressions for live human computer interaction. In: 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, pp. 351–356. IEEE (2007)
Duncan, D., Shine, G., English, C.: Facial Emotion Recognition in Real Time, pp. 1–7. Stanford University (2016)
Dagar, D., Hudait, A., Tripathy, H.K., Das, M.N.: Automatic emotion detection model from facial expression. In: 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 77–85. IEEE (2016)
Goodfellow, I.J., Erhan, D., Carrier, P.L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.H., Zhou, Y.: Challenges in representation learning: a report on three machine learning contests. In: International Conference on Neural Information Processing, pp. 117–124. Springer, Berlin (2013)
Bouazizi, M., Ohtsuki, T.: Sentiment analysis: from binary to multi-class classification: a pattern-based approach for multi-class sentiment analysis in Twitter. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2016)
Viola, P., Jones, M.: Robust real-time face detection. In: Null, pp. 747–751. IEEE (2001)
Arriaga, O., Valdenegro-Toro, M., Plöger, P.: Real-time convolutional neural networks for emotion and gender classification (2017). arXiv:1710.07557
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Manning, C.D., Raghavan, P., Schütze, H.: Scoring, term weighting and the vector space model. Introd. Inf. Retr. 100, 2–4 (2008)
Fatima, S.A., Kumar, A., Raoof, S.S.: Real time emotion detection of humans using Mini-Xception algorithm. In: IOP Conference Series: Materials Science and Engineering, vol. 1042, No. 1, p. 012027. IOP Publishing (2021)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Patel, N., Patel, F., Kumar Bharti, S. (2022). Live Emotion Verifier for Chat Applications Using Emotional Intelligence. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_2
Download citation
DOI: https://doi.org/10.1007/978-981-16-6616-2_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6615-5
Online ISBN: 978-981-16-6616-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)