Lie-Sensor: A Live Emotion Verifier or a Licensor for Chat Applications using Emotional Intelligence

TL;DR

This paper introduces Lie-Sensor, an emotion verification system for chat apps that uses facial expressions and text analysis to detect deception and promote honest communication.

Contribution

It presents a novel live emotion verifier combining CNN-based facial emotion recognition with SVM-based text classification for message authenticity verification.

Findings

CNN miniXception model achieved high accuracy in facial emotion detection.

SVM classifier provided the best performance for message text prediction.

The system effectively distinguishes between genuine and fraudulent messages.

Abstract

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.