Tech Stack: Python, Flask, Bootstrap, Pickle
The Emotion Classification Web App Dashboard is a powerful tool designed to analyze real-time scraped tweet data and classify emotions using advanced techniques such as TF-IDF for information retrieval and Support Vector Machines (SVM) as the classification algorithm. The dashboard, built using the intuitive Bootstrap framework, provides a user-friendly interface for easy interaction.
Behind the scenes, a robust model is created using Python, leveraging the cutting-edge capabilities of TF-IDF for efficient information retrieval. This technique allows the model to identify the most important words and phrases in the tweets, enabling accurate emotion classification. The model is trained and fine-tuned using a state-of-the-art SVM algorithm, which excels at handling complex classification tasks.
To ensure seamless integration with the web app, the trained model is serialized and stored using the Pickle module, optimizing performance and enabling quick access to the classification functionality.
The Emotion Classification Web App Dashboard empowers users to gain valuable insights into the emotions expressed in real-time tweet data, providing a comprehensive and intuitive interface for interaction and analysis.
Code: https://github.com/zemosi/emotion-classification-dashboard/
