In-Depth Survey Insight Analysis Workflow
This workflow automates the processing of survey data by identifying similar response groups through vector storage and K-means clustering algorithms. It combines large language models for summarization and sentiment analysis, and finally exports the results to Google Sheets. This process is efficient and precise, capable of deeply mining potential patterns in text responses. It is suitable for scenarios such as market research, user experience surveys, and academic research, helping users quickly extract key insights and enhance the scientific and timely nature of decision-making.
Tags
Workflow Name
In-Depth Survey Insight Analysis Workflow
Key Features and Highlights
This workflow automates the process of importing large volumes of survey response data, vectorizing and storing them, and leveraging the K-means clustering algorithm to identify groups of similar answers. It then uses large language models (LLMs) to summarize and perform sentiment analysis on the clustering results. Finally, high-quality insights are automatically exported to Google Sheets. The workflow is well-structured, supports large-scale participant data processing, and ensures that the analysis results are detailed and actionable.
Core Problems Addressed
Traditional survey data analysis often relies on manual work or simple statistics, making it difficult to deeply uncover latent patterns and nuances within large volumes of textual responses. This workflow utilizes vectorization and clustering techniques to automatically discover and summarize commonalities among respondents’ answers, avoiding overly generic summaries. It enhances the depth and accuracy of data insights and effectively tackles the challenges of analyzing large-scale survey data.
Application Scenarios
- Automated data analysis for large-scale market research and user experience surveys
- Intelligent insight extraction from internal employee satisfaction or remote work experience surveys
- In-depth questionnaire data analysis in academic research
- Any scenario requiring extraction of specific, detailed insights from large volumes of textual survey responses
Main Process Steps
- Import Survey Response Data: Retrieve survey answers from Google Sheets and convert them into question-answer pairs.
- Vectorization: Use OpenAI text embedding models to vectorize answer texts and store them in the Qdrant vector database.
- Trigger Sub-Workflow Analysis: Process each question in batches and retrieve relevant answer vectors.
- Clustering Analysis: Apply a Python-implemented K-means algorithm to cluster answer vectors, identifying representative answer groups.
- Cluster Filtering: Filter clusters to retain only those containing three or more answers.
- Insight Generation and Sentiment Analysis: Use OpenAI GPT-4 to summarize answers within each cluster, extract insights, and determine overall sentiment orientation.
- Create and Export Insight Sheets: Create a new “Insights” worksheet in the original survey Google Sheets and append the insight results for centralized data management.
Involved Systems or Services
- Google Sheets: Source of raw survey data and storage for analyzed insights
- OpenAI API (Text Embeddings and GPT-4): Enables text vectorization and intelligent summarization and analysis
- Qdrant: Vector database for storing survey answer vectors, supporting efficient similarity search and clustering
- Python Code Nodes: Execute the K-means clustering algorithm on vector data
- n8n Workflow Triggers and Nodes: Automate the process flow and invoke sub-workflows
Target Users and Value
- Data analysts and market researchers, facilitating rapid extraction of key insights from large volumes of textual survey data
- Product managers and user experience researchers, improving the depth and efficiency of user feedback analysis
- Teams and enterprises aiming to automate processing of employee satisfaction or remote work survey results
- Any users needing to handle large-scale textual survey data and obtain structured analytical outcomes
This workflow enables users to perform high-quality survey data analysis based on vectorization and clustering without complex programming, significantly enhancing the scientific rigor and timeliness of decision support.
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