📊 AI-Powered Sentiment Analysis Web App: Understanding Emotions Through AI

Artificial Intelligence is rapidly reshaping the way we understand human emotions. From product reviews to social media posts, analyzing text to detect positivity or negativity has become an essential part of digital intelligence. The Sentiment Analysis Web App, developed by Mirza Yasir Abdullah Baig, is an innovative AI-powered platform that predicts whether a given text expresses a positive or negative sentiment — combining both Machine Learning (ML) and Deep Learning (DL) techniques into one interactive web solution.

This project demonstrates how natural language processing (NLP) and deep learning can come together to interpret emotions in text data, making it ideal for students, developers, and researchers exploring text-based AI.

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🚀 Overview

The Sentiment Analysis Web App is an intelligent application built with Streamlit — a Python framework for creating interactive AI web apps. It allows users to input text and instantly receive a sentiment prediction, indicating whether the sentiment is positive 😊 or negative ☹️.

What makes this app unique is its dual-model support: users can switch between a Machine Learning model (.pkl) and a Deep Learning model (.keras), showcasing two different approaches to text analysis in a single platform.

🔍 Key Features

• Dual Model Capability:
  Choose between a classic Machine Learning model and a modern Deep Learning model for comparison.
• Instant Sentiment Prediction:
  Detects whether a given sentence is positive or negative within seconds.
• Confidence Score:
  Displays the model’s confidence level (ranging from 0 to 1), helping users understand how certain the AI is.
• Streamlit Interface:
  The app’s interface is minimal, responsive, and designed for easy navigation.
• About Me Sidebar:
  Includes author information and portfolio links for professional connection.

🧠 How It Works

The sentiment analysis process involves several NLP and AI steps behind the scenes. Here’s a breakdown:

1. Input Text:
   The user types or pastes text into the Streamlit input form.
2. Preprocessing:
   • The text is tokenized (split into words).
   • Converted into numerical sequences that AI models can understand.
   • Padded to ensure all sequences have a uniform length.
3. Model Selection:
   • ML Model (.pkl): Uses Scikit-learn’s pre-trained pipeline for text classification (e.g., Logistic Regression or Naive Bayes).
   • DL Model (.keras): Uses TensorFlow/Keras-based neural network trained on text embeddings.
4. Prediction:
   • The selected model processes the input and outputs a sentiment score.
   • A score > 0.5 → Positive sentiment 😊
   • A score ≤ 0.5 → Negative sentiment ☹️
5. Output:
   • Displays sentiment result and confidence score instantly.
   • Results are visually formatted for better readability.

⚙️ Tech Stack Used

The project combines traditional and deep learning techniques, offering flexibility and innovation through the following technologies:

• Python 3.9+ – Core programming language
• Streamlit – For building the interactive web app
• NumPy & Pandas – For data processing and management
• TensorFlow / Keras – For the Deep Learning model
• Scikit-learn & Joblib – For the Machine Learning model and model serialization
• Pickle (.pkl) and Keras (.keras) formats – For storing pre-trained models

This hybrid architecture makes the project both educational and practical for demonstrating AI’s versatility in sentiment analysis.

💻 Project Structure

Here’s how the repository is organized for easy understanding:

This structure ensures clarity and reusability for developers who wish to extend or customize the project.

📸 Screenshots

The app includes a collection of screenshots that demonstrate its elegant design and functionality:

• 🏠 Home Page: A clean and minimal input interface.
• 😊 Positive Sentiment Prediction: Displays happy emoji and high confidence score.
• ☹️ Negative Sentiment Prediction: Displays sad emoji and corresponding score.

Each page highlights the model’s responsiveness and the simplicity of the UI, making it accessible even for non-technical users.

🎥 Demo

The project includes both a live Streamlit app and a video demonstration:

• 🔗 Live Demo: Hosted on Streamlit for instant access.
• 🎥 Video Demo: Sentiment-Analysis.webm showcasing predictions in real-time.

Users can easily explore how AI models interpret emotional tones from text samples.

📄 How the Models Differ

1. Machine Learning Model (.pkl)

• Based on traditional NLP techniques such as TF-IDF (Term Frequency–Inverse Document Frequency).
• Uses Scikit-learn algorithms like Logistic Regression or SVM.
• Lightweight and fast, ideal for smaller datasets.

2. Deep Learning Model (.keras)

• Uses word embeddings and neural network layers.
• Built with TensorFlow/Keras, capable of understanding context and word relationships.
• Performs better on large and complex datasets.

This dual setup helps users learn how both classical and neural approaches tackle sentiment classification differently.

👨‍💻 Developer Information

Author: Mirza Yasir Abdullah Baig
An experienced AI and ML developer passionate about building intelligent web applications that merge usability with advanced data science.

• 💼 LinkedIn: [Profile Link]
• 🌐 Kaggle: [Profile Link]
• 💻 GitHub: mirzayasirabdullahbaig07

❤️ Acknowledgements

Special thanks to the open-source tools and resources that made this project possible:

• TensorFlow/Keras – For deep learning model development
• Scikit-learn – For traditional ML pipelines
• Streamlit Documentation – For enabling rapid web app creation

⚠️ Disclaimer

This project is designed for educational and research purposes.
It is not intended for commercial use or large-scale production.
The app serves as a learning example of how AI can interpret human sentiment through text-based analysis.

📄 License

This project is open-source and available under the MIT License, allowing anyone to use, modify, and improve it for personal or academic purposes.

🏁 Conclusion

The Sentiment Analysis Web App by Mirza Yasir Abdullah Baig is a perfect example of how Artificial Intelligence bridges human language and technology. By combining Machine Learning and Deep Learning, it showcases the evolution of text sentiment prediction — from simple word frequency analysis to complex neural understanding.

Whether you’re analyzing movie reviews, tweets, or customer feedback, this app provides a powerful, easy-to-use platform to understand emotional tones at scale. It’s not just an AI project — it’s a glimpse into the future of human-computer emotional interaction.

Explore the Project:
👉 Sentiment Analysis Model on GitHub

Developed with ❤️ by Mirza Yasir Abdullah Baig