n8n Twitter ETL Sentiment Pipeline: MongoDB to Postgres + Slack

Turn Twitter chatter into a daily sentiment feed—saved to MongoDB & Postgres, and shared in Slack

This n8n workflow collects tweets every day, stores them in MongoDB, analyzes sentiment with Google Cloud Natural Language, writes the results to Postgres, and posts positive tweets to a Slack channel—while ignoring negative ones.

What this workflow does

• Cron node (daily schedule): Runs the pipeline once per day.
• Twitter node: Collects tweets for your defined query.
• MongoDB node: Inserts the collected tweets into MongoDB.
• Google Cloud Natural Language node: Analyzes each tweet’s sentiment.
• Set node: Extracts the sentiment score and magnitude from the analysis.
• Postgres node: Inserts tweets plus sentiment score and magnitude into a Postgres database.
• IF node: Filters tweets based on sentiment (positive vs. negative).
• Slack node: Posts tweets with a positive sentiment score to a Slack channel.
• NoOp node: Ignores tweets with a negative sentiment score.

Use cases

• Monitor public reactions to a product and surface “good news” instantly in your team’s Slack.
• Build a lightweight sentiment history by persisting tweets and sentiment signals in MongoDB and Postgres.
• Automate daily social listening for SaaS operations without manual spreadsheets.

Technical details

• n8n nodes: Cron, Twitter, MongoDB, Google Cloud Natural Language, Set, Postgres, IF, Slack, NoOp
• Data flow: Twitter → MongoDB → sentiment analysis → extract score/magnitude → Postgres storage → IF filter → Slack post