ManualTrigger Automate
For HackerNews, this manual-triggered workflow automates the extraction and analysis of comments from a selected story, leveraging advanced clustering and AI insights. It efficiently organizes comments into meaningful groups, identifies key community sentiments, and exports detailed insights to Google Sheets, enhancing understanding of user feedback and trends.
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Workflow Overview
For HackerNews, this manual-triggered workflow automates the extraction and analysis of comments from a selected story, leveraging advanced clustering and AI insights. It efficiently organizes comments into meaningful groups, identifies key community sentiments, and exports detailed insights to Google Sheets, enhancing understanding of user feedback and trends.
Target Audience
- Data Analysts: Individuals looking to analyze community feedback on Hacker News stories.
- Developers: Those who want to integrate Hacker News data into their applications or workflows.
- Researchers: Academics and professionals studying online community dynamics and sentiment.
- Product Managers: Professionals seeking insights into user opinions and trends based on comments from Hacker News.
- Marketers: Individuals interested in understanding customer sentiment and feedback from tech-savvy communities.
Problem Solved
This workflow addresses the challenge of extracting, analyzing, and summarizing comments from Hacker News stories. It automates the process of gathering comments, clustering them for insights, and generating actionable summaries, thereby saving time and providing valuable community insights without manual effort.
Workflow Steps
- Manual Trigger: Initiates the workflow when the user clicks 'Test workflow'.
- Set Variables: Defines the
story_idfor the Hacker News article to be analyzed. - Clear Existing Comments: Deletes any previous comments associated with the story ID in the Qdrant vector store to ensure fresh data.
- Fetch Comments: Uses the Hacker News API to retrieve comments for the specified story, including nested replies.
- Split Out Comments: Separates the retrieved comments into individual entries for further processing.
- Store Comments in Qdrant: Inserts the comments into the Qdrant vector store for efficient querying and analysis.
- Find Comments: Retrieves comments from the Qdrant vector store based on the provided story ID.
- Apply K-means Clustering: Groups comments into clusters based on their content to identify common themes.
- Get Payload of Points: Fetches the detailed content of the clustered comments from Qdrant.
- Generate Insights: Uses a language model to summarize the clustered comments and determine the overall sentiment.
- Prep Output: Formats the insights and comments for export.
- Export to Google Sheets: Appends the insights and comments to a specified Google Sheet for easy access and sharing.