Build an AI Stock Sentiment Analyzer with Gemini 2.0 & Python

This tutorial walks you through how to create a streamlined AI sentiment monitoring pipeline of social media data that leverages Google’s Gemini 2.0 and Python to analyze financial chatter around specific stocks. You’ll learn how to pull recent posts from Blue Sky, extract essential metadata, and invoke a Large Language Model (LLM) to evaluate sentiment.

We’ll use Python libraries such as requests, pandas, typing-extensions, google-generativeai, and plotly to demonstrate a straightforward workflow that can be adapted for a range of use cases beyond stock sentiment, from brand sentiment tracking to political sentiment analysis.

This tutorial provides a blueprint, not an exhaustive solution. If you’re a data scientist, machine learning engineer, or AI engineer looking to refine your own sentiment-tracking methodologies, you can easily integrate additional features such as filtering by specific keywords, batching queries, or scaling to multiple social networks. You can also experiment with alternative LLM providers—for instance, OpenAI’s GPT, Anthropic's Claude, Mistral, open-source models via Ollama, or Google’s other Gemini models—or customize the prompt to fit your preferred sentiment definitions and domain-specific language.

***Important Note: This video is not financial or investing advice. Everything shown is for educational purposes only, highlighting how to leverage cutting-edge AI and LLM techniques. Always apply critical thinking and incorporate subject matter expertise into your decision-making process—LLMs are experimental and can produce inaccurate results.***

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*Full Code*
Github: https://github.com/deepcharts/project...

*Resources*
Google Gemini Developer API Site: https://ai.google.dev/
Google Models Pricing: https://ai.google.dev/pricing#1_5flash
Bluesky: https://bsky.app/
Bluesky searchPosts doc: https://docs.bsky.app/docs/api/app-bs...

*Chapters*
0:00 - Intro
0:35 - How to Create Bluesky and Google AI Studio Accounts
1:50 - Python Environment Setup
2:05 - Jupyter Notebook setup, credentials, and api configurations
3:28 - Pulling and Processing data from Bluesky using Bluesky API
5:18 - AI Sentiment Analysis with Structured Outputs using Google's Generative AI python package
9:08 - Basic Methodology for Creating Daily Sentiment Score for a Stock