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https://github.com/ibensusan/customer-reviews-analysis
A Python program to analyze customer review data using Pandas and NLTK.
https://github.com/ibensusan/customer-reviews-analysis
Last synced: 8 days ago
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A Python program to analyze customer review data using Pandas and NLTK.
- Host: GitHub
- URL: https://github.com/ibensusan/customer-reviews-analysis
- Owner: iBensusan
- License: mit
- Created: 2024-11-17T10:52:53.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-11-18T19:46:22.000Z (about 1 month ago)
- Last Synced: 2024-11-18T20:46:40.773Z (about 1 month ago)
- Size: 2.93 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Customer Reviews Analysis with Pandas and NLTK
A Python program designed to analyze customer review data using Pandas and NLTK. This program loads review data, performs text cleaning and sentiment analysis, and summarizes the data by calculating average ratings for positive, neutral, and negative reviews. It demonstrates data processing, natural language processing, and insights extraction using sentiment scores.
## Features
- **Data Loading**: Efficiently loads customer review data from a CSV file using Pandas.
- **Sentiment Analysis**: Classifies reviews as positive, neutral, or negative using NLTK's Sentiment Intensity Analyzer.
- **Data Summary**: Computes the average rating for each sentiment category (positive, neutral, and negative).
## Requirements
- Python 3.x
- Pandas
- NLTK
Install the required libraries with:
```bash
pip install pandas nltk
```
## Installation
1. Clone this repository or download the project files:
```bash
git clone https://github.com/username/customer-reviews-analysis.git
cd customer-reviews-analysis
```
2. Verify you have Python 3 installed:
```bash
python --version
```
3. Install the required dependencies:
```bash
pip install pandas nltk
```
4. Download the NLTK resources needed for sentiment analysis:
```bash
python -m nltk.downloader vader_lexicon
```
## Usage
1. Place the `reviews.csv` file in the project directory.
2. Run the analysis script:
```bash
python analyze_reviews.py
```
3. The program will output:
- Average ratings grouped by sentiment (positive, neutral, negative).
- A detailed sentiment analysis summary for each review in the dataset.
## Example Workflow
1. Start the program by running the script.
2. The script loads the `reviews.csv` file and analyzes the sentiment of each customer review.
3. The average ratings for positive, neutral, and negative reviews are displayed.
4. A summary of the sentiment analysis for individual reviews is printed.
## Files
- `analyze_reviews.py`: The main Python script implementing the data analysis and sentiment classification.
- `reviews.csv`: The dataset containing customer reviews and corresponding ratings.
- `README.md`: Project description and detailed usage instructions.
## Professional Use Cases
This project can be adapted for various real-world scenarios, such as:
1. **Customer Feedback Analysis**: Helps businesses understand customer sentiment and identify areas of improvement based on reviews.
2. **Market Research**: Analyzes public reviews to gain insights into customer preferences and pain points.
3. **Product Development**: Uses sentiment analysis to prioritize new features or fixes based on customer feedback.
## License
This project is licensed under the MIT License.