ISY503 Intelligent Systems - Project

This project involves training a neural network to perform sentiment analysis on a dataset of labeled product reviews, classifying them as positive or negative. The trained model is integrated into a web application for real-time sentiment analysis using FastAPI for the backend and a simple JavaScript page for entering reviews and displaying results.

Prerequisites

Before you begin, make sure you have the following installed:

• Python 3.7 or higher
• pip (Python package installer)

Setup and Installation

1. Clone the repository:

git clone https://github.com/sergey-chek/sentiment-analysis-isy503.git

cd sentiment-analysis-isy503

2. Create a virtual environment:

python3 -m venv venv

In some cases it could be

python -m venv venv

3. Activate the virtual environment:

- Windows:

.\venv\Scripts\activate

- MacOS and Linux:

source venv/bin/activate

4. Install dependencies:

pip install -r requirements.txt

Running the Web Application

1. Start the FastAPI server using Uvicorn:

The following command will start the FastAPI server on http://127.0.0.1:8000/ by default.

uvicorn main:app --reload

Note

If you use a different port for the server, make sure to update the API_ENDPOINT variable in static/script.js to match the correct port.

2. Access the frontend

Open your web browser and navigate to http://127.0.0.1:8000/ (or to your custom endpoint).

Building Model

1. Ensure that the training data files are placed in the model/import-data folder.
2. To prepare a combined .csv file and perform spelling correction, run the model.prepare module using command:

python3 -m model.prepare

3. For subsequent data cleaning, run the model.clean module using command:

python3 -m model.clean

4. For model training, run the model.train module using command:

python3 -m model.train

The trained model will be saved in the model/trained-model directory.

License

This project is licensed under the MIT License.

Acknowledgments

This project was developed as part of the ISY503 Intelligent Systems course at Torrens University of Australia.