Twitter Sentiment to Postgres and Slack

Name: Twitter Sentiment to Postgres and Slack
Rating: 1.0 (1 reviews)
Author: Free N8N

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Data Integrationautomationetlnlppostgressentiment analysisslacktwitter

Fetches tweets, analyzes sentiment, stores in PostgreSQL, and notifies Slack on high-sentiment tweets.

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About This Workflow

Overview

This workflow automates the process of fetching tweets related to a specific hashtag, analyzing their sentiment using Google Cloud Natural Language, storing the tweet text and sentiment scores in a PostgreSQL database, and sending notifications to Slack for tweets with a significant positive sentiment.

Key Features

Fetches tweets using the Twitter API.
Analyzes sentiment (score and magnitude) of tweets using Google Cloud Natural Language.
Stores tweet text and sentiment data in a PostgreSQL database.
Inserts tweet data into a MongoDB collection.
Sends notifications to Slack for tweets exceeding a defined sentiment score threshold.

How To Use

Configure Credentials: Set up credentials for Twitter, PostgreSQL, MongoDB, Google Cloud Natural Language, and Slack in n8n.
Set Twitter Search: In the Twitter node, configure the searchText with the desired hashtag (e.g., =#OnThisDay). Adjust the limit as needed.
Configure MongoDB Insert: In the MongoDB node, specify the collection (e.g., tweets) and the fields to insert.
Configure Google Cloud NLP: Ensure the content in the Google Cloud Natural Language node correctly references the tweet text from the MongoDB output (e.g., ={{$node["MongoDB"].json["text"]}}).
Configure Set Node: In the Set node, map the sentiment score and magnitude from the Google Cloud NLP output to new fields. Also, ensure the text field is populated with the original tweet text.
Configure Postgres Node: In the PostgreSQL node, specify the table (e.g., tweets) and the columns to store (text, score, magnitude). The returnFields should be set to =*.
Configure If Node: Set the condition in the If node to filter tweets based on sentiment score (e.g., ={{$json["score"]}} > 0.5). Adjust the threshold as necessary.
Configure Slack Node: In the Slack node, customize the text to include the tweet's score, magnitude, and content. Set the target channel.
Error Handling: The workflow includes an error handler for robust execution.

Apps Used

automation

etl

nlp

postgres

sentiment analysis

slack

twitter

Workflow JSON

{
  "id": "a40a3b01-f3a8-4945-8ea2-88c771f3b574",
  "name": "Twitter Sentiment to Postgres and Slack",
  "nodes": 0,
  "category": "Data Integration",
  "status": "active",
  "version": "1.0.0"
}

Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.