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Analyzing Twitter Data Using Large Language Models: A Step-by-Step Tutorial

Updated: Feb 25


In this video tutorial, you'll learn how to leverage Large Language Models (LLM) like OpenAI to analyze Twitter data comprehensively. Utilizing Twitter data and the Python code provided on GitHub, you'll follow a structured approach to extract, process, and analyze tweets with advanced techniques.


  • Understand how to utilize Large Language Models for Twitter data analysis.

  • Gain hands-on experience in executing Python code for data extraction and analysis.

  • Learn to perform sentiment analysis, language translation, emotion identification, entity extraction, and summarization using OpenAI.


  1. Access MongoDB Atlas and locate the Tweet collection.

  2. Retrieve the connection string for Python from MongoDB Atlas.

  3. Log in to AWS Academy, initiate the learner lab, and launch a Notebook instance on SageMaker.

  4. Modify the config.ini file to include MongoDB and OpenAI API credentials.

  5. Download and upload Python code to the Notebook instance.

  6. Execute Python code step by step, ensuring consistency with database and collection names.

  7. Query Twitter data from MongoDB and extract for analysis, utilizing MongoDB Compass for query composition.

  8. Employ OpenAI for comprehensive tweet analysis, including sentiment, translation, emotion identification, and entity extraction.

  9. Manage notebook instance and learner lab upon completion of analysis.

  10. Utilize MongoDB Charts to visualize analysis results, create charts for sentiment and emotion, extract persons and organizations, and tweet summaries.

  11. Publish the dashboard, ensuring accessibility and functionality.

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