Python Sentiment Analysis Using LLMs: A Step-by-Step Tutorial
Learn how to perform sentiment analysis in Python by leveraging the capabilities of Large Language Models (LLMs). This practical, step-by-step tutorial guides you through the process of setting up your environment, making API calls (or using local models), and interpreting the results for effective text analysis.
We explore how LLMs can provide a nuanced understanding of sentiment (Positive, Negative, Neutral, and beyond) compared to traditional methods. This guide is designed for developers and data analysts interested in integrating modern NLP techniques into their Python workflows.
What You'll Learn in This Video:
An overview of sentiment analysis and the role of LLMs.
Setting up your Python environment with necessary libraries.
Interfacing with popular LLM providers or libraries (e.g., Hugging Face Transformers, OpenAI).
Writing Python functions to send text and receive sentiment scores.
Processing text data for analysis.
Handling and interpreting the output from LLMs.
Practical code examples demonstrating the workflow.
Timestamps:
0:00 - Introduction to Sentiment Analysis in Python & LLMs
0:23 - Getting started
1:18 - Processing a Python DataFrame using an LLM
3:56 - Using the DataFrame processed by AI for further analysis in Python
5:52 - Conclusion
Resources:
Getting started with Fabi.ai: https://app.fabi.ai/
AI cells documentation: https://docs.fabi.ai/workflow_automat...
How-to blog: https://www.fabi.ai/blog/how-to-use-p...
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