AcademyWorkflowsData and reporting

ETL pipeline for text processing

Data and social media teams that want a stored record of sentiment for mentions on X, with positive posts shared in Slack.

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etl-pipeline-for-text-processing.json

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What it does

This n8n workflow collects tweets from X on a daily schedule and stores them in MongoDB. It scores each tweet's sentiment with Google Cloud Natural Language and saves the text with its score and magnitude in a Postgres database. Tweets with a positive score are posted to a Slack channel, and tweets with a negative score are ignored.

Use cases

  1. 01Store daily mentions of a brand in a database
  2. 02Score tweet sentiment for a periodic review
  3. 03Share positive posts with a team channel

Connects

Postgres · Slack · MongoDB · X (Formerly Twitter) · Google Cloud Natural Language

Questions about
ETL pipeline for text processing.

What is ETL pipeline for text processing used for?

Data and social media teams that want a stored record of sentiment for mentions on X, with positive posts shared in Slack. This n8n workflow collects tweets from X on a daily schedule and stores them in MongoDB. It scores each tweet's sentiment with Google Cloud Natural Language and saves the text with its score and magnitude in a Postgres database. Tweets with a positive score are posted to a Slack channel, and tweets with a negative score are ignored.

How do I import ETL pipeline for text processing into n8n?

Copy the workflow JSON from this page and paste it onto the n8n canvas with Ctrl+V or Cmd+V. Then connect your credentials in each node.

Is ETL pipeline for text processing safe to use?

It passed our automatic scan for credential theft, hidden instructions and risky install commands. Third party open source software. Sabemos AI does not maintain it. Check the code and permissions before you connect it to company data.

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