Sentiment Analysis Using Transformers

Overview

This project utilizes Hugging Face's Transformers library to perform sentiment analysis on financial news headlines. The model predicts the sentiment of given text and provides a confidence score for its classification.

Features

• Uses Hugging Face Transformers to analyze sentiment.
• Implements the BERT-based multilingual sentiment model (nlptown/bert-base-multilingual-uncased-sentiment).
• Processes a list of financial news headlines and classifies their sentiment.
• Outputs the predicted sentiment label and confidence score.

Technologies Used

• Python
• Hugging Face Transformers

Installation

Prerequisites

Ensure you have Python (version 3.x) installed on your system.

Steps

1. Clone the repository:

   git clone https://github.com/yourusername/sentiment-analysis.git

2. Navigate to the project directory:

   cd sentiment-analysis

3. Install the required dependencies:

   pip install transformers

4. Run the script:

   python sentiment_analysis.py

Usage

• The script contains a predefined list of financial headlines.
• It processes each headline using the sentiment analysis model.
• The predicted label and confidence score are displayed in the console.

Example Output

Headline 1: Predicted Label - 4 stars, Confidence Score - 0.85 - Microsoft-Activision deal back in hands of UK regulator after court pauses appeal
Headline 2: Predicted Label - 2 stars, Confidence Score - 0.76 - SBB, Brookfield end talks on EduCo stake sale, shares tumble
...

Customization

• You can modify the model_name variable to use a different sentiment analysis model from Hugging Face.
• Update the headlines list with your own text inputs.

License

MIT License © 2024 Your Name