Sentiment Analysis Chatbot Using Hugging Face Transformers

This project is a simple Sentiment Analysis Chatbot built using the Hugging Face Transformers library. It detects whether a user's input conveys a positive or negative sentiment and responds accordingly. The chatbot also recognizes basic greetings and farewells, providing intelligent and emoji-enhanced responses.

Project Structure

├── chatbot.py # Main chatbot logic ├── requirements.txt # Python dependencies └── README.md # Project documentation

Features

• Sentiment Analysis: Classifies user input as positive, negative, or neutral using Hugging Face's pipeline for sentiment analysis.
• Basic Intent Recognition: Recognizes common greetings like "Hi" and farewells like "Goodbye" for a more natural conversation.
• Emoji Integration: Uses emojis to enhance chatbot responses, making the interactions more engaging.

Technologies Used

• Python: Core programming language.
• Hugging Face Transformers: NLP library for using pre-trained models.
• Streamlit: Web app framework for deploying the chatbot (optional).

Setup Instructions

1. Clone the repository

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

2. Create a virtual environment (Optional but recommended)

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

3. Install the required dependencies

pip install -r requirements.txt

4. Run the Chatbot Locally

You can run the chatbot locally using Python. In the terminal, run the following command: python chatbot.py

5. Deploy the Chatbot on Streamlit (Optional)

To showcase this project to recruiters, you can deploy it using Streamlit Cloud.

1. Install Streamlit if you haven’t already:

   pip install streamlit

2. Modify your chatbot script to work with Streamlit (if necessary).

3. Run the following command to launch it on Streamlit locally: streamlit run chatbot.py

4. If you want to deploy it, link your GitHub repository to Streamlit Cloud and deploy the app directly.

Demo on Streamlit Cloud