A Multi-Modal, Cross-Platform Personality Analysis System with Deception Detection

The rapid advancement of artificial intelligence has enabled automated behavioral analysis through multimodal data processing. This paper presents A Multi-Modal, Cross-Platform Personality Analysis System with Deception Detection, an intelligent framework capable of interpreting human personality traits using text, image, and social media inputs. The system integrates Natural Language Processing (NLP) with computer vision, employing BERT for sentiment-based personality estimation and DeepFace for emotion and gender recognition from facial images. A Flask-based backend coordinates the analysis pipeline, while a React.js frontend delivers an interactive, cross-platform dashboard for real-time insights. MongoDB Atlas is used as a scalable cloud database for storing user interactions and analysis history. The proposed system provides a comprehensive behavioral profile by combining multimodal features using a weighted fusion strategy. Experimental evaluation demonstrates high accuracy, with BERT achieving up to 98% sentiment reliability and DeepFace reaching 97% emotion recognition accuracy. The overall system performance is measured at 98%, confirming the robustness and effectiveness of the multi-modal approach. This work contributes to emerging applications in e-recruitment, digital forensics, mental-health assessment, and smart human–computer interaction, demonstrating the potential of AI-driven personality understanding in real-world scenarios.