Amazon Fine Food Reviews Sentiment Analysis Project

Overview

This project is focused on performing sentiment analysis on Amazon Fine Food Reviews using natural language processing techniques. The goal is to build a model that can accurately predict whether a review is positive or negative based on the text content.

Dataset

The dataset used in this project is the Amazon Fine Food Reviews dataset, which can be obtained from the following link: Amazon Fine Food Reviews Dataset

The dataset consists of reviews for various food products available on Amazon, along with corresponding ratings and textual reviews. The data has been preprocessed to remove any personal information.

Project Structure

The project repository is organized as follows:

|-- data/
|   |-- Reviews.csv
|
|-- notebooks/
|   |-- Exploratory_Data_Analysis.ipynb
|   |-- Data_Preprocessing.ipynb
|   |-- Sentiment_Analysis_Model.ipynb
|
|-- src/
|   |-- data_loader.py
|   |-- data_preprocessor.py
|   |-- sentiment_analysis.py
|
|-- models/
|   |-- sentiment_model.pkl
|
|-- README.md
|-- requirements.txt

• data: Contains the dataset file Reviews.csv.
• notebooks: Jupyter notebooks for various stages of the project, such as data exploration, data preprocessing, and building the sentiment analysis model.
• src: Python source code for data loading, data preprocessing, and the sentiment analysis model.
• models: The trained sentiment analysis model stored as sentiment_model.pkl.
• README.md: The readme file you are currently reading.
• requirements.txt: A list of required Python libraries and their versions.

Getting Started

1. Clone this repository to your local machine using:

git clone https://github.com/your-username/amazon-fine-food-reviews-sentiment-analysis.git
cd amazon-fine-food-reviews-sentiment-analysis

2. Install the required dependencies using pip:

pip install -r requirements.txt

3. Download the dataset from the provided Kaggle link and place it in the data folder.

4. Launch Jupyter Notebook and explore the project notebooks in the notebooks directory to understand the project workflow.

Data Preprocessing

Before building the sentiment analysis model, the textual data needs to be preprocessed. This involves steps such as removing stop words, tokenization, and stemming or lemmatization. The Data_Preprocessing.ipynb notebook in the notebooks directory demonstrates this process.

Sentiment Analysis Model

The sentiment analysis model is built using machine learning techniques to predict whether a review is positive or negative. The Sentiment_Analysis_Model.ipynb notebook in the notebooks directory contains the model building process.

Using the Trained Model

Once the model is trained, you can use it to perform sentiment analysis on new textual data. The trained model is stored in models/sentiment_model.pkl. An example of how to use the model is provided in the Sentiment_Analysis_Model.ipynb notebook.

Contributing

If you would like to contribute to this project, feel free to open issues, suggest improvements, or submit pull requests. Your contributions are greatly appreciated!

License

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

---

Feel free to update and customize the above template according to your actual project details. Add more information about the model's performance, evaluation metrics, and any other relevant details.