Sentiment Analysis App

A compact Streamlit web app that demonstrates multiple approaches to sentiment and emotion analysis for text, images and IMDb movie reviews.

What it does

• Text: Analyze sentiment and emotion from free-form user text using multiple libraries.
• IMDb movie reviews: Fetch reviews for a movie and analyze collective sentiment/emotions.
• Image: Detect faces in an uploaded image and estimate emotions per face and for the whole image.

Key features

• Multiple sentiment engines: TextBlob, VADER (NLTK), Flair, text2emotion for richer comparisons.
• Image emotion detection with FER and OpenCV face detection.
• Simple multi-page Streamlit UI with a sidebar navigator and reusable modal components.
• Plotting and visualization using Plotly / Matplotlib and PIL for image handling.

Project layout

• app.py — Streamlit entry point and page routing
• sidebar.py — Sidebar navigation and shared controls
• textPage.py — Text input UI and sentiment processing helpers
• imdbReviewsPage.py — IMDb review fetch + batch analysis UI
• imagePage.py — Image upload, face detection, image-level and per-face emotion analysis
• modals.py — Reusable modal dialogs and wrappers for sentiment/image libraries
• images/ — Static assets
• requirements.txt / packages.txt — Python dependencies

Dependencies & Python version

• Python recommended: 3.8+ (some NLP/vision packages have limited wheels for newer versions)
• See requirements.txt for the full list (streamlit, flair, textblob, nltk, text2emotion, fer, opencv-python, pillow, plotly, pandas, requests, etc.)

How it works

• Text: input text → run multiple analyzers → normalize results → show label, scores and visualizations.
• IMDb: query movie → fetch reviews → batch-process with selected analyzers → aggregate and visualize sentiment distribution. ( 2 IMDb API requests per Analysis → 100 API calls per day Limit )
• Image: upload image → detect faces with OpenCV → run FER/emotion model per face and for full image → display bounding boxes and emotion charts.