Reddit Stock Sentiment Analyzer 🧠📈

This is a full-stack web application that performs sentiment analysis on up to 100 recent Reddit posts about a stock symbol using machine learning models with FinBERT embeddings and additional text features. It is built with a FastAPI backend and a React (Vite) frontend.

✨ Features

• 🔍 Fetches and displays up to 100 latest matching posts from multiple finance subreddits.
• 🧠 Classifies each post as Positive, Neutral, or Negative using trained ML models.
• 📈 Uses FinBERT (pre-trained on financial text) for generating embeddings.
• 📊 Incorporates additional text features like word count and VADER sentiment scores.
• 🛡️ Includes backend API error handling and frontend request timeout/error messaging.
• ⚡ Built with FastAPI for robust backend performance.
• ⚛️ Clean and fast React interface via Vite.

📸 Screenshots

🚀 Getting Started

1. Clone the Repository

git clone https://github.com/anismabaziz/stock-mood
cd stock-mood

2. Backend Setup

# Navigate to backend directory
cd backend

# Install uv (if you don't have it yet)
# macOS/Linux: curl -LsSf https://astral.sh/uv/install.sh | sh

# Sync dependencies (creates .venv automatically)
uv sync

Python note: backend dependencies are pinned for Python 3.11/3.12 compatibility.

3. Environment Configuration

Create a .env file in the backend directory:

reddit_client_id='your reddit client_id'
reddit_client_secret='your reddit client_secret'
reddit_user_agent='your reddit user_agent'
frontend_url='frontend dev url'
frontend_build='frontend production url'

Create a .env file in the frontend directory:

VITE_API_URL='backend dev url'

4. Frontend Setup

# Navigate to frontend directory
cd ../frontend

# Install dependencies
npm install

🏃‍♂️ Running the Application

Start Backend Server

cd backend
uv run uvicorn main:app --reload

Backend URLs:

• Health check: http://127.0.0.1:8000/ping
• OpenAPI docs: http://127.0.0.1:8000/docs

Start Frontend Development Server

cd frontend
npm run dev

If the frontend cannot reach the backend, verify frontend/.env:

VITE_API_URL=http://127.0.0.1:8000

Then restart the frontend dev server.

🧠 Machine Learning Model

Model Selection & Testing

I tested multiple models to find the best performer:

• Logistic Regression: Baseline linear classifier
• Random Forest: Ensemble method with decision trees
• Gradient Boosting: Sequential ensemble learning
• SVM: Support Vector Machine with probability estimation
• XGBoost: Gradient boosting framework
• MLP (scikit-learn): Multi-Layer Perceptron classifier
• Custom Neural Network: Self-implemented deep learning model - Selected Model

Feature Engineering

• FinBERT Embeddings: Contextualized embeddings from financial domain pre-trained BERT
• Text Statistics: Word count and other linguistic features
• VADER Sentiment: Rule-based sentiment analysis scores as additional features
• Training Data: Labeled financial news articles (not Reddit-specific)
• Model Accuracy: ~80% on the validation set

The final model uses a custom-built neural network that combines FinBERT's powerful contextual understanding with traditional text features and VADER sentiment scores to achieve robust sentiment classification on financial discussions.

🛠️ Tech Stack

Frontend:

• React (Vite)
• Axios

Backend:

• FastAPI
• PRAW
• FinBERT (via HuggingFace Transformers)
• VADER Sentiment (via NLTK)
• scikit-learn / TensorFlow (for ML models)
• XGBoost