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ETL Pipeline

This workflow automates the extraction of tweets on specific topics from Twitter, conducts sentiment analysis using natural language processing, and stores the results in MongoDB and Postgres databases. It is triggered on a schedule to ensure real-time data updates, while intelligently pushing important tweets to a Slack channel based on sentiment scores. This process not only enhances data processing efficiency but also helps the team respond quickly to changes in user sentiment, optimize content strategies, and improve brand reputation management. It is suitable for social media operators, marketing teams, and data analysts.

Workflow Diagram
ETL Pipeline Workflow diagram

Workflow Name

ETL Pipeline

Key Features and Highlights

This workflow captures tweets on a specific topic (#OnThisDay) from Twitter, performs sentiment analysis using Google Cloud Natural Language API, automatically stores data in MongoDB and Postgres databases, and intelligently pushes important tweets to a designated Slack channel based on sentiment scores. The entire process is automated and efficient, supporting scheduled triggers to ensure real-time data updates.

Core Problems Addressed

  • Automates the acquisition and processing of social media data, eliminating the need for manual scraping and analysis
  • Conducts sentiment analysis on tweets to quantify emotional tendencies and intensity, aiding decision-making
  • Automatically stores analysis results in structured databases for easy querying and reporting
  • Filters high-value content based on conditional logic and promptly notifies the team, enhancing response speed

Use Cases

  • Social media data monitoring and public opinion analysis
  • Real-time insights into trending topics and user sentiment for marketing teams
  • Rapid capture of critical feedback for customer service and public relations departments
  • Building sentiment analysis datasets for data analysts to support subsequent model training

Main Process Steps

  1. Triggered daily at 6 AM to fetch the latest 3 tweets tagged with #OnThisDay from Twitter
  2. Write tweet text into MongoDB as raw data storage
  3. Perform sentiment analysis on tweet content using Google Cloud Natural Language API, extracting sentiment scores and magnitude
  4. Store sentiment analysis results along with tweet text into structured tables in the Postgres database
  5. Evaluate tweet value based on sentiment scores; if the score is high, send the tweet content and analysis results to a specified Slack channel, otherwise take no action

Involved Systems and Services

  • Twitter API (tweet retrieval)
  • MongoDB (raw tweet data storage)
  • Google Cloud Natural Language (sentiment analysis)
  • Postgres Database (structured storage of analysis results)
  • Slack (notification of high-value tweets)
  • Cron Scheduler (workflow scheduled triggering)

Target Users and Value

  • Social Media Managers: Obtain and analyze key topic tweets in real-time to optimize content strategy
  • Data Analysts and Data Engineers: Build automated data pipelines integrating data collection and sentiment analysis
  • Marketing and PR Teams: Quickly respond to shifts in user sentiment, enhancing brand reputation management
  • Technical Teams: Integrate multiple services to create flexible ETL workflows, improving automation levels

This ETL pipeline workflow provides enterprises with an efficient solution for social media sentiment monitoring and data support through automated data collection, analysis, and notification.