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yashreddy1154 / repository
This repository contains a Python-based sentiment analysis project using Jupyter Notebook to analyze customer reviews. The project leverages TextBlob for sentiment classification, pandas for data manipulation, and Matplotlib/Seaborn for visualizing insights. Key features include: Sentiment Classification,Statistical Analysis
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Welcome to my sentiment analysis project! This repository contains a Python-based analysis of customer reviews using Jupyter Notebook, designed to classify sentiments and uncover insights through data visualization and statistical analysis. The project leverages popular libraries like TextBlob, pandas, Matplotlib, and Seaborn to process and visualize review data.
This project analyzes a dataset of customer reviews to determine sentiment (Positive, Negative, or Neutral) and explores relationships between sentiment and metrics such as helpfulness votes, review timing, and calculated scores. The goal is to provide actionable insights into customer satisfaction and feedback trends, showcasing skills in data science, natural language processing (NLP), and data visualization.
Sentiment Distribution (Count Plot)
Helpful Votes by Sentiment (Bar Chart)
Positive Review Word Cloud
Correlation Matrix (Heatmap)
days_since_first_review and day_diff).Ensure you have Python 3.x installed, along with the following libraries:
pandasmatplotlibseabornnltktextblobwordcloudYou can install these using pip:
pip install pandas matplotlib seaborn nltk textblob wordcloud
git clone https://github.com/yashreddy1154/amazon-sentiment-analysis
cd amazon-sentiment-analysis
amazon.csv) and place it in the repository root directory.code.ipynb) in Jupyter Notebook or VS Code with the Jupyter extension.code.ipynb: Main Jupyter Notebook with the complete analysis and visualizations.amazon.csv: Dataset containing review data (ensure this matches your file name).README.md: This file, providing project documentation.reviewText to ensure clean input for TextBlob.plt.tight_layout() and higher DPI.days_since_first_review and day_diff, prompting further investigation into dataset structure.Have questions or suggestions? Connect with me on LinkedIn or open an issue in this repository!