Lie-Sensor: A Live Emotion Verifier or a Licensor for Chat Applications using Emotional Intelligence
This addresses the need for promoting honest conversations in chat apps, but it is incremental as it combines existing methods for emotion detection and text classification.
The paper tackles the problem of verifying emotional honesty in live chat applications by comparing facial emotion labels (Happiness, Sadness, Surprise, Hate) detected via CNN with text emotion labels predicted via SVM, and it reports achieving the best accuracy with SVM on the training dataset.
Veracity is an essential key in research and development of innovative products. Live Emotion analysis and verification nullify deceit made to complainers on live chat, corroborate messages of both ends in messaging apps and promote an honest conversation between users. The main concept behind this emotion artificial intelligent verifier is to license or decline message accountability by comparing variegated emotions of chat app users recognized through facial expressions and text prediction. In this paper, a proposed emotion intelligent live detector acts as an honest arbiter who distributes facial emotions into labels namely, Happiness, Sadness, Surprise, and Hate. Further, it separately predicts a label of messages through text classification. Finally, it compares both labels and declares the message as a fraud or a bonafide. For emotion detection, we deployed Convolutional Neural Network (CNN) using a miniXception model and for text prediction, we selected Support Vector Machine (SVM) natural language processing probability classifier due to receiving the best accuracy on training dataset after applying Support Vector Machine (SVM), Random Forest Classifier, Naive Bayes Classifier, and Logistic regression.