Mobile app for emotion detection.

Overview: I designed and developed an innovative mobile chat app that tracks users’ emotions based on the text they type, suggesting emojis consistent with their emotional state. The app also features a dashboard that provides an overview of users’ emotions throughout the day.

Key Contributions:

1. Emotion Classification:

   • Data Preparation: Cleaned and preprocessed training datasets to ensure accuracy and reliability in the emotion classification task.
   • Model Implementation: Implemented the Word2Vec model to analyze and understand the emotional context of user messages.

2. Mobile App Development:

   • Framework: Developed the mobile application using Flutter, ensuring a seamless and responsive user experience across various devices.
   • User Interface: Designed an intuitive and engaging interface that allows users to easily interact with the app and view their emotional dashboard.

3. Backend and Deployment:

   • API Development: Deployed the emotion classification model and built API endpoints using Flask, enabling real-time emotion tracking and emoji suggestions.
   • Integration: Ensured smooth integration between the frontend and backend, providing users with accurate and timely feedback on their emotional state.

Impact: This project showcases my ability to combine data science and mobile development skills to create a unique and user-centric application. It highlights my expertise in emotion analysis, natural language processing, and mobile app development, demonstrating my capability to deliver innovative solutions that enhance user engagement and experience.