SentiraAI: An Integrated Framework for Real-Time Sentiment Analysis and Cognitive Behavioral Support

Abstract

In recent years, the growing awareness of mental health and emotional well-being has highlighted the need for accessible, technology-driven support systems that can assist individuals in understanding and managing their emotions. Sentira is a web-based emotion-aware application developed with the objective of combining affective computing and conversational artificial intelligence to provide emotional awareness and mental well-being support in an interactive and user-friendly manner. The system focuses on detecting human emotions through facial expressions and enabling emotionally responsive interaction through a chatbot interface. The application employs camera-based facial emotion recognition to identify common emotional states such as happiness, sadness, anger, neutrality, and surprise. These emotions are analyzed in real time and presented visually to the user using confidence bars and expressive emojis, allowing users to gain immediate insight into their emotional state. This visual representation enhances transparency and improves user engagement by making emotion analysis easy to interpret, even for non-technical users. The emotion detection feature is designed to function seamlessly within a web environment, emphasizing responsiveness and accessibility. In addition to emotion detection, Sentira incorporates an intelligent chatbot designed to interact with users in a supportive and empathetic manner. The chatbot processes user input and generates emotion-sensitive responses along with motivational or reflective quotations sourced from well-known thinkers and authors. This feature aims to provide emotional reassurance, encouragement, and positive reinforcement, thereby extending the system’s role beyond simple emotion classification to emotional support and engagement. The chatbot acts as a conversational companion that encourages users to express their thoughts and feelings in a safe, non-judgmental digital space. From a technical perspective, Sentira is implemented using a modular frontend–backend architecture based on modern web technologies. The backend manages API communication, request validation, and structured data handling, while the frontend delivers an interactive and visually intuitive user experience. The system design emphasizes maintainability, scalability, and clean separation of concerns, making it suitable for further enhancements such as persistent storage, advanced emotion models, or extended conversational intelligence. Overall, Sentira demonstrates how emotion recognition and conversational systems can be integrated into a single platform to support emotional awareness and mental well-being. The project highlights the practical application of affective computing concepts in real-world web applications and serves as a foundation for future research and development in emotion-aware human–computer interaction.

Introduction

The text presents Sentira, a web-based emotion detection and conversational support system designed to promote emotional awareness and mental well-being. It addresses the growing emotional stress and mental fatigue experienced in modern digital life, alongside the lack of accessible, non-judgmental mental health support tools. Leveraging advances in artificial intelligence, affective computing, and web technologies, Sentira integrates facial emotion recognition and an emotion-aware chatbot into a single platform.

Sentira detects basic emotions—happiness, sadness, anger, neutrality, and surprise—through real-time facial analysis using a device camera and represents them using confidence indicators and expressive emojis. Alongside this, an empathetic chatbot allows users to express their feelings through text and responds with supportive messages and motivational quotations tailored to the emotional context. The system is intended as a supplementary self-awareness tool, not a replacement for professional mental health services.

The project outlines clear aims and objectives focused on accessibility, usability, emotional reflection, and scalability through a modular frontend–backend web architecture. A literature survey highlights the effectiveness of facial emotion recognition, emotion-aware conversational systems, and their role in mental well-being support, noting a gap in fully integrated platforms.

The problem statement emphasizes limitations in existing solutions, such as poor emotional sensitivity, lack of intuitive feedback, and fragmented functionality. Sentira addresses these challenges by offering a unified, user-friendly system. Its architecture follows a client–server model, with distinct modules for user interaction, emotion detection, chatbot communication, backend processing, data handling, and response delivery. The implementation focuses on real-time interaction, simplicity, and responsiveness, demonstrating the practical integration of emotion recognition and conversational interfaces in a web-based environment while providing a foundation for future enhancements.

Conclusion

The development of Sentira, an emotion-aware chatbot and facial emotion detection system, successfully demonstrates the integration of modern web technologies with intelligent emotion analysis to support mental well-being. This project was designed with the objective of understanding human emotions through both visual cues and textual interaction, and the final system fulfills this objective effectively. By combining real-time emotion detection with an empathetic chatbot, Sentira provides users with an interactive and supportive digital experience. Throughout the project, emphasis was placed on creating a system that is simple to use, responsive, and emotionally aware. The emotion detection module enables real-time identification of facial expressions such as happiness, sadness, anger, surprise, neutrality, and related emotional states, which are visually represented using confidence bars and emojis. This visual feedback allows users to better understand their emotional state in an intuitive manner. Alongside this, the chatbot module enhances user interaction by responding to text input with emotionally appropriate messages and motivational quotes, making the interaction more human-centric rather than purely technical. From a technical perspective, Sentira demonstrates a clear separation of concerns between the front-end and back-end components. The front-end provides an interactive user interface, while the back-end manages API routing, request validation, and data handling in a structured and scalable manner. The use of modern frameworks and libraries ensures maintainability, modularity, and smooth data flow across the system. Even though the project adopts a simplified storage mechanism and rule-based response logic, it effectively showcases the practical implementation of full-stack development concepts. Academically, this project serves as a strong example of how theoretical knowledge can be applied to solve real-world problems. Concepts such as client–server architecture, RESTful APIs, data validation, modular design, and system integration are clearly reflected in the implementation. Sentira also highlights the growing importance of emotionally intelligent systems in today’s digital landscape, especially in areas related to mental health awareness and user-centric computing. In conclusion, Sentira stands as a complete, functional, and meaningful application that aligns well with the objectives of a seventh-semester academic project. While there is scope for future enhancements such as advanced machine learning models, persistent databases, and improved emotion accuracy, the current system successfully fulfills its intended purpose. The project not only demonstrates technical competence but also reflects thoughtful design, innovation, and relevance to modern societal needs. Sentira therefore represents a solid foundation for further research and development in the domain of emotion-aware intelligent systems.

References

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Copyright

Copyright © 2026 Kanak Akre, Payal Mahajan, Rahul Paswan, Umama Sheikh , Mr. Y. B. Malode. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.