survey-analyzer

To analyze survey responses, you can perform Likert scale analysis, cross-tabulations, and frequency distributions, then generate visualizations like charts, heatmaps, and word clouds using matplotlib and seaborn.

Yes, you can generate comprehensive PDF reports from customer satisfaction survey data using the reportlab library, transforming raw responses into actionable insights with sentiment scoring and statistical analysis.

Sentiment analysis on open-ended survey responses uses TextBlob to score text data, allowing you to identify key drivers of satisfaction and dissatisfaction across different demographic groups.

Statistical methods for survey data cross-tabulation include frequency distributions and statistical testing using scipy, enabling you to evaluate relationships between different demographic variables.

Yes, pandas and numpy are required dependencies for data manipulation and statistical analysis, alongside scipy, textblob, matplotlib, seaborn, wordcloud, and reportlab for full functionality.

The best way to visualize survey frequency distributions is by generating charts and heatmaps with seaborn, and creating word clouds from text responses to easily identify common themes.