Amazon-Baby-Product-Reviews-Sentiment-Analysis

Amazon Baby Product Reviews Sentiment Analysis

This project applies a K-Nearest Neighbors (KNN) classifier to analyze sentiment in Amazon baby product reviews. The primary objective is to predict whether a review is positive, neutral, or negative based on the text content.

Project Overview

The project involves the following steps:

1. Data Loading and Preprocessing: Load the dataset, handle missing values, and create sentiment labels.
2. Feature Extraction: Convert the text reviews into numerical features using TF-IDF vectorization.
3. Model Training: Train a KNN classifier on the processed data.
4. Prediction and Evaluation: Predict sentiment labels for the test set, evaluate the model’s performance, and visualize the results.
5. Visualization: Generate plots to illustrate the distribution of predicted and true ratings, as well as the confusion matrix.

Key Features

• Data Handling: Preprocess and clean the review data.
• Feature Engineering: Use TF-IDF vectorization for transforming text data.
• Modeling: Implement KNN for classification.
• Evaluation: Calculate accuracy, mean squared error, and generate a classification report.
• Visualization: Create visual representations of data distributions and model performance.

Installation

To run this project, ensure you have the following packages installed:

• numpy
• pandas
• matplotlib
• seaborn
• scikit-learn

You can install these packages using pip:

pip install numpy pandas matplotlib seaborn scikit-learn

Usage

1. Clone the repository:

    git clone https://github.com/yourusername/amazon-baby-reviews-sentiment-analysis.git
    cd amazon-baby-reviews-sentiment-analysis
   

2. Run the Python script:

    python sentiment_analysis.py
   

Results

• Accuracy: The overall accuracy of the model.
• Mean Squared Error: The MSE of the model predictions.
• Confusion Matrix: A heatmap showing the confusion matrix.
• Rating Distributions: Histograms showing the distribution of predicted and true ratings.

Conclusion

This project demonstrates a basic application of machine learning for sentiment analysis of product reviews. The KNN classifier provides a simple yet effective approach for classifying review sentiments.

Profile

Check out my GitHub profile: Esmail Sarhadi

Project Link

View this project on GitHub: Amazon-Baby-Product-Reviews-Sentiment-Analysis

Donation

If you find this project helpful, consider making a donation:

License

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

Acknowledgements

• Dataset: Amazon baby product reviews
• Libraries: numpy, pandas, matplotlib, seaborn, scikit-learn

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