EmoTrack sets out to transform emotion analysis in text data. Our mission is to create an application capable of analyzing diverse text inputs, such as social media posts and messages, to discern the writer's mood or emotional state. This involves collecting text data, preprocessing it for sentiment analysis, training advanced models to recognize emotions, and seamlessly integrating this functionality into an intuitive application interface.
- Bilingual Text Analysis: Develop algorithms for analyzing text in multiple languages, enabling global emotion sensing.
- Multi-modal Data Processing: Implement mechanisms to process diverse data types, including text, images, and audio, for comprehensive emotion analysis.
- Fine-grained Emotion Recognition: Train models to recognize nuanced emotions, capturing subtle variations in human expression.
- Real-time Emotion Detection: Enable real-time analysis of emotions, providing instantaneous insights into users' emotional states.
- Visual Emotion Representation: Utilize visual aids to represent emotions, enhancing user comprehension and engagement.
- Privacy-preserving Emotion Analysis: Implement robust encryption techniques to protect users' data privacy while performing emotion analysis.
- Bilingual Text Analysis: EmoTrack transcends language barriers, offering emotion analysis capabilities across various languages.
- Multi-modal Data Processing: By integrating text, image, and audio processing, EmoTrack provides a holistic approach to emotion analysis.
- Fine-grained Emotion Recognition: Our models excel at identifying intricate emotional nuances, providing a comprehensive understanding of users' sentiments.
- Real-time Emotion Detection: EmoTrack delivers instantaneous emotion analysis, enabling prompt responses and interventions.
- Visual Emotion Representation: Through visualizations and graphical representations, EmoTrack enhances user interaction and comprehension of emotional insights.
- Privacy-preserving Emotion Analysis: We prioritize user privacy by implementing state-of-the-art encryption techniques, ensuring sensitive data remains secure during analysis.
- AI Model (TensorFlow): Leveraging TensorFlow for training and deploying emotion recognition models, utilizing BERT and BiLSTM architectures for optimal performance.
- JWT Token-based Authorization: Implementing secure authentication mechanisms using JWT tokens to authenticate users and protect sensitive data.
- SHA256 Encryption: Employing SHA256 encryption for secure data transmission and storage, safeguarding user privacy.
- Flask Backend: Developing a robust backend infrastructure using Flask, enabling seamless integration with frontend applications.
- ReactJS: Building dynamic and responsive user interfaces with ReactJS, ensuring an engaging user experience.
- OpenCV (for Facial Image Recognition): Utilizing OpenCV for facial image recognition, enabling emotion analysis based on facial expressions.
- Hamburger (Audio to Text): Integrating Hamburger for audio-to-text conversion, facilitating emotion analysis from spoken input.
- Clone the Repository: Clone the EmoTrack repository to your local machine.
- Install Dependencies: Navigate to the project directory and install the required dependencies using
npm installfor the frontend andpip install -r requirements.txtfor the backend. - Set Up Backend: Configure the Flask backend by setting up database connections, JWT secret keys, and other environment variables as needed.
- Run the Application: Start the backend server using
python app.pyand launch the frontend usingnpm start. - Explore Emotion Analysis: Interact with the EmoTrack application to analyze text, images, and audio inputs, gaining valuable insights into emotional states.
We welcome contributions from the community to enhance EmoTrack's functionality and expand its capabilities further. Feel free to submit pull requests, report issues, or suggest new features through our GitHub repository.
Let's revolutionize emotion analysis together with EmoTrack!
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