https://github.com/jokoum/machine-learning-project
Semester project for the Machine Learning class of the MSc in Artificial Intelligence
https://github.com/jokoum/machine-learning-project
jupyter-notebook machine-learning python sentiment-classification sklearn
Last synced: 3 months ago
JSON representation
Semester project for the Machine Learning class of the MSc in Artificial Intelligence
- Host: GitHub
- URL: https://github.com/jokoum/machine-learning-project
- Owner: JoKoum
- Created: 2020-12-29T16:04:56.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-07-07T21:08:01.000Z (almost 2 years ago)
- Last Synced: 2025-02-26T06:35:40.092Z (over 1 year ago)
- Topics: jupyter-notebook, machine-learning, python, sentiment-classification, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 4.66 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Predict users product reviews sentiment
---
#### Semester project for the Machine Learning class of the [MSc in Artificial Intelligence](http://msc-ai.iit.demokritos.gr/) by NCSR Demokritos and University of Piraeus
The project evaluates the performance of different Machine Learning Classification algorithms over predicting the correct sentiment (Positive or Negative) of the given review in **Greek** language.
The [Amazon Cell Phones Reviews](https://www.kaggle.com/grikomsn/amazon-cell-phones-reviews) reviews CSV file from Kaggle was the initial dataset. After preprocessing and artificially translating the data, more dataset files were created.
- Used datasets can be found [here](https://drive.google.com/drive/folders/1-WGObbkfur67vylKkT7mdXsxOMB7czOx?usp=sharing). You can load them directly via the notebook as well.
Trained model is tested against actual product reviews taken from [Skroutz](https://www.skroutz.gr)
##### You can clone the project from repository using the following command:
git clone https://github.com/JoKoum/Machine-Learning-Project.git
##### You can create a new virtual environment and install the dependencies using the requirements.txt file:
On macOS and Linux:
python3 -m venv
On Windows:
py - m venv
Package installation using requirements.txt file:
pip install -r requirements.txt
##### more info in [pip and virtual environments](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)
[Jupyter Notebook Viewer link](https://nbviewer.jupyter.org/github/JoKoum/Machine-Learning-Project/blob/main/Analyze-review-sentiment.ipynb)