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🎭 CaptAI™

CaptAI™ is an intelligent sentiment analysis system powered by dual machine learning models trained on a 50,000-review IMDb dataset.
It enables users to automatically analyze audience sentiment in movie reviews, classifying them as Positive or Negative with high confidence and interpretability.


CaptAI Logo

--- ## 🚀 Key Features
  • Dual sentiment analysis models:
    • Model Alekxia
    • Model Almirax
  • Adjustable prediction confidence threshold
  • Keyword-based sentiment explanation
  • Confidence score for each prediction
  • Automatic word cloud visualization
  • Clean and interactive Streamlit interface

🧠 Use Case

CaptAI is designed for:

  • Movie review sentiment analysis
  • Audience opinion mining
  • NLP demonstrations and research
  • Educational and portfolio showcase purposes

🛠️ How to Use CaptAI

  1. Launch the application
  2. Select your preferred sentiment analysis model from the sidebar:
    • Model Alekxia or Model Almirax
  3. (Optional) Adjust the prediction confidence threshold
  4. Enter a movie review text (maximum of 200 words)
  5. Click Analyze Sentiment
  6. View the results:
    • Predicted sentiment (Positive or Negative)
    • Model confidence score
    • Summary of influential keywords
    • Word cloud of dominant terms

📊 Output Explanation

The system provides:

  • Predicted Sentiment: Final classification result
  • Confidence Score: Model certainty for the prediction
  • Keyword Summary: Words influencing the decision
  • Word Cloud: Visual representation of prominent terms

🔗 Live System

👉 Try the Live App Here


🧩 Technologies Used

  • Python
  • Scikit-learn
  • Natural Language Processing (NLP)
  • Streamlit
  • IMDb Movie Reviews Dataset

👤 Developer

Anafi Abdul-Azeez Olohunjuwon
AI/ML & LLM Developer | Research Writer

About

CaptAI is a dual-architecture machine learning model designed to analyze embedded sentiments in textual data such as movie reviews, opinions, and user feedback. By combining two complementary learning components, the system improves sentiment detection accuracy and captures nuanced emotional patterns within natural language.

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