local-llama3-sentiment-analysis

This tutorial will guide you through the steps to set up and run a sentiment analysis project using Python. The project includes analyzing comments from a CSV file, sending them to an Ollama API for sentiment analysis, and visualizing the sentiment distribution.

Prerequisites

• Python 3.10 or higher
• pip (Python package installer)
• Ollama API setup and running on http://localhost:11434

Project Setup

1. Clone the Repository

First, clone the repository to your local machine:

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

2. Create a Virtual Environment

Create and activate a virtual environment to manage dependencies:

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

3. Install Dependencies

Install all the required Python packages listed in requirements.txt:

pip install -r requirements.txt

4. Running the Project

You can now run the main script to start the sentiment analysis:

python3 main.py

Project Structure

• main.py: The main script that handles reading comments from a CSV file, sending them to the Ollama API for sentiment analysis, and visualizing the sentiment distribution.
• requirements.txt: A file listing all the Python dependencies.

5. Preparing the CSV File

Make sure you have a CSV file named commentsEng.csv in the same directory as main.py. The CSV file should have a column named commentText containing the comments to be analyzed.

Running the Project

After setting up everything, you can run the project using:

python3 main.py

This will analyze the comments from the CSV file, print the comments with their detected sentiments, display a bar chart of the sentiment distribution, and print the CPU usage.

Contributing

Feel free to submit issues and pull requests. For major changes, please open an issue first to discuss what you would like to change.

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