Sephora Skincare Reviews Sentiment Analysis

This project performs sentiment analysis on Sephora skincare reviews using a pre-trained DistilBERT model fine-tuned on a custom dataset.

Introduction

Sentiment analysis is a natural language processing technique used to determine the sentiment expressed in a piece of text. In this project, we use a pre-trained DistilBERT model from Hugging Face to perform sentiment analysis on Sephora skincare product reviews. The model is fine-tuned on a custom dataset to improve performance on the specific domain.

Dataset

The dataset used in this project is available on Kaggle: Sephora Products and Skincare Reviews.

The dataset contains various columns, such as product_id, user_id, rating, and review_text. For our sentiment analysis task, we focus on the review_text column.

Dependencies

To run this project, you will need the following libraries:

• Python 3.7 or later
• PyTorch 1.9.0 or later
• TorchText 0.6.0
• Transformers 4.0.0 or later
• Pandas
• CUDA (if using a GPU)

Installation

1. Clone this repository:

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

2. Change to the project directory:

cd sephora-skincare-reviews-sentiment-analysis

3. Install the required libraries:

pip install -r requirements.txt

4. Download the dataset from Kaggle and place it in the data folder.

Usage - (as Python Project in local env)

1. Preprocess the dataset by running preprocess_data.py:

python preprocess_data.py

This script will split the dataset into training, validation, and test sets. 2. Fine-tune the DistilBERT model using the training data by running train.py:

python train.py

3. Evaluate the model on the test data by running evaluate.py:

python evaluate.py

4. To perform sentiment analysis on new data, use the predict_sentiment function in the predict.py script.

Usage - (as Notebook .ipynb project)

1. Download or open the .ipynb file in a notebook of your choice

2. Run each cell in a sequential order

3. To perform sentiment analysis on new data, use the predict_sentiment function from the dedicated cell at the end

License

This project is licensed under the MIT License. See the LICENSE file for details.