ETL pipeline for text processing

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

This workflow allows you to collect tweets, store them in MongoDB, analyse their sentiment, insert them into a Postgres database, and post positive tweets in a Slack channel. ![workflow screenshot]...

Best for

Market Research automation workflows
AI Summarization automation workflows
intermediate n8n builders looking for reusable templates

Tools used

n8n-nodes-base.twitter, n8n-nodes-base.postgres, n8n-nodes-base.mongodb, n8n-nodes-base.slack, n8n-nodes-base.if, n8n-nodes-base.noop, n8n-nodes-base.googlecloudnaturallanguage, n8n-nodes-base.set

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Lorena.

Original n8n.io source

1.1 Workflow description

Title: ETL pipeline for text processing
Workflow name: ETL pipeline for text processing

This workflow allows you to collect tweets, store them in MongoDB, analyse their sentiment, insert them into a Postgres database, and post positive tweets in a Slack channel.

Cron node: Schedule the workflow to run every day

Twitter node: Collect tweets

MongoDB node: Insert the collected tweets in MongoDB

Google Cloud Natural Language node: Analyse the sentiment of the collected tweets

Set node: Extract the sentiment score and magnitude

Postgres node: Insert the tweets and their sentiment score and magnitude in a Posgres database

IF node: Filter tweets with positive and negative sentiment scores

Slack node: Post tweets with a positive sentiment score in a Slack channel

NoOp node: Ignore tweets with a negative sentiment score

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Twitter

Type / Role: n8n-nodes-base.twitter - twitter
Config choices: Version 1

Block 2 - Postgres

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 1

Block 3 - MongoDB

Type / Role: n8n-nodes-base.mongoDb - mongoDb
Config choices: Version 1

Block 4 - Slack

Type / Role: n8n-nodes-base.slack - slack
Config choices: Version 1

Block 5 - IF

Type / Role: n8n-nodes-base.if - if
Config choices: Version 1

Block 6 - NoOp

Type / Role: n8n-nodes-base.noOp - noOp
Config choices: Version 1

Block 7 - Google Cloud Natural Language

Type / Role: n8n-nodes-base.googleCloudNaturalLanguage - googleCloudNaturalLanguage
Config choices: Version 1

Block 8 - Set

Type / Role: n8n-nodes-base.set - set
Config choices: Version 1

Block 9 - Cron

Type / Role: n8n-nodes-base.cron - cron
Config choices: Version 1

3. Summary Table

Workflow	ETL pipeline for text processing
Complexity	intermediate
Nodes	9
Categories	Market Research, AI Summarization
Author	Lorena
Published	19 Apr 2021

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/1045/1045.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Market Research workflows AI Summarization workflows

Frequently asked questions

What does ETL pipeline for text processing do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Market Research, AI Summarization use case.

Lorena

26 workflows

Nodes

n8n-nodes-base.twitter n8n-nodes-base.postgres n8n-nodes-base.mongodb n8n-nodes-base.slack n8n-nodes-base.if n8n-nodes-base.noop n8n-nodes-base.googlecloudnaturallanguage n8n-nodes-base.set

Complexity

intermediate

Published 19 Apr 2021

Likes 0

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Install path: /data/workflows/1045/1045.json

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ETL pipeline for text processing

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Twitter

Block 2 - Postgres

Block 3 - MongoDB

Block 4 - Slack

Block 5 - IF

Block 6 - NoOp

Block 7 - Google Cloud Natural Language

Block 8 - Set

Block 9 - Cron

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions