📈 Stock Sentiment Analysis Dashboard

🌐 Overview

This project aims to provide real-time sentiment analysis on publicly traded companies. The focus is on the quality of the data and the accuracy of the machine learning model used for sentiment analysis.

📊 Data

📡 Real-Time News Data

Real-time news data is sourced from Alpha Vantage and processed for sentiment analysis.

📚 Training Data

The machine learning model is trained on a dataset of financial news articles. The dataset undergoes rigorous cleaning and preprocessing to improve the model's performance.

🧹 Data Cleaning and Preprocessing

• 🔠 Tokenization: Sentences are tokenized into individual words.
• 🧼 Text Cleaning: All text is converted to lowercase, and non-alphanumeric characters are removed.
• ✂️ Stemming: Words are stemmed using the Porter Stemming algorithm.

🤖 Model

🛠 Algorithm and Libraries

The model uses a Random Forest classifier implemented with scikit-learn and leverages Word2Vec for feature extraction, implemented using the Gensim library.

🎛 Feature Extraction

Word2Vec is used to convert sentences into vectors, serving as features for the model.

🏋️‍♀️ Model Training

The model is trained using 80% of the data, with the remaining 20% reserved for testing. The model is then saved for future use.

🖥 Interface

The project includes a minimalistic interface for users to input a stock ticker and receive sentiment analysis results.

🚀 Usage

To run the project:

1. 📦 Clone the repository
2. 🛠 Install dependencies
3. 🏃‍♂️ Run flask run

👨‍💻 Developed By

Developed by Yuval Moscovitz