Automated Twitter Sentiment Analysis Pipeline

This n8n workflow automates the process of collecting, analyzing, storing, and sharing Twitter data based on specific hashtags. It begins by scheduling a daily trigger at 6 AM. Once triggered, it searches Twitter for recent tweets tagged with ‘#OnThisDay’. Each retrieved tweet is then processed to assess its sentiment score and magnitude using Google’s Natural Language API. The sentiment data, along with the tweet text, is stored in a PostgreSQL database for future analysis.

For tweets with a positive sentiment score, the workflow sends a highlighted message to a Slack channel, showcasing the tweet’s text and sentiment details. The process of sentiment analysis involves inserting tweet text into a MongoDB collection and then analyzing this text using Google Cloud Natural Language API. Based on the sentiment score, the workflow includes a conditional check (IF node) to determine whether to send a Slack notification.

This automated pipeline is ideal for social media monitoring, brand reputation management, or tracking public sentiment on specific topics without manual effort, ensuring timely insights and communication.