sentiment.js

An AI-powered text analysis tool that performs sentiment analysis, keyword extraction, and term relevance scoring against a fixed design vocabulary. Analyze dialogue, reviews, feedback, or any textual content to extract insights about emotions, key topics, and relevance to design terms.

Features

• Sentiment Analysis: Detects 27 different emotions using RoBERTa-based emotion classification
• Keyword Extraction: Automatically extracts important keywords and phrases using KeyBERT
• Term Relevance: Analyze relevance of a fixed design vocabulary in the text

Quick Start

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

Installation

1. Clone this repository:

   git clone https://github.com/thomknoe/sentiment.js.git
   cd sentiment.js

2. Create and activate a virtual environment (recommended):

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Python dependencies:

   pip install -r requirements.txt

   Or install manually:

   pip install flask flask-cors transformers torch torchvision keybert sentence-transformers scikit-learn

4. Run the Flask backend server:

   python backend.py

   The backend will start on http://127.0.0.1:5001

5. Open the frontend:

   • Option A: Use VS Code's "Go Live" extension or any local server
   • Option B: Open index.html directly in a browser (voice recording won't work without HTTPS)
   • Option C: Use Python's built-in server:

     python -m http.server 8000

     Then open http://localhost:8000 in your browser

6. Start analyzing text!