This project analyzes customer feedback to derive key attributes, perform sentiment analysis, classify the relevance of statements, and generate comprehensive business reports using Quality Function Deployment (QFD) methodologies.
-
Clone the Repository:
git clone https://github.com/tochman/ai_qfd_customer_attributes cd ai_qfd_customer_attributes -
Create a Virtual Environment:
python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
-
Install Dependencies:
pip install -r requirements.txt
-
Set Up Environment Variables:
Create a
.envfile in the project root.Add your OpenAI API key:
OPENAI_API_KEY=your_openai_api_key_here
-
Prepare Data Files:
Ensure
survey.txtandsurvey_details.txtare populated with relevant data.survey.txt: A text file containing customer statements, one per line.survey_details.txt: A text file containing details about the survey, such as objectives, methodology, and demographics.
Run the main script with optional arguments:
python main.py --domain "Healthcare Services" --input_file "./survey.txt" --survey_details_file "./survey_details.txt" --log_level "INFO"Command-Line Arguments:
--domain: The domain for analysis (default: "Healthcare Services").--input_file: Path to the customer statements file (default: "./survey.txt").--survey_details_file: Path to the survey details file (default: "./survey_details.txt").--log_level: Set the logging level (choices: DEBUG, INFO, WARNING, ERROR, CRITICAL; default: INFO).
main.py: Entry point of the application.config.py: Configuration settings and logging setup.models.py: Pydantic models for data validation.data_loading.py: Functions to load data from files.chains.py: LangChain chain configurations, including relevance classification, sentiment analysis, and attribute derivation.analysis.py: Data analysis functions, such as calculating relative importance.visualization.py: Visualization functions for generating charts and graphs.report.py: Report generation functions.utils.py: Utility functions.requirements.txt: Python dependencies..env: Environment variables (not committed to version control).data/: Directory containing data files.data/survey.txt: Sample customer statements.data/survey_details.txt: Sample survey details.
To analyze customer feedback in the domain of "Healthcare Services" using the provided survey.txt and survey_details.txt files:
python main.py --domain "Healthcare Services" --input_file "./data/survey.txt" --survey_details_file "./data/survey_details.txt" --log_level "INFO"The script will generate a business report in docx format.
Logs are output to the console with the specified logging level. Adjust the logging level using the --log_level argument to get more or less verbose output.
- Ensure your OpenAI API key has sufficient permissions and usage limits to handle the data volume.
- The script uses OpenAI's GPT-4 model; make sure your API key has access to this model.
- Be mindful of the token limits when processing large datasets. Adjust batch sizes in
config.pyif necessary.
This project is licensed under the MIT License.