Sentiment Analysis with MindsDB and OpenAI using SQL

This tutorial demonstrates how to perform sentiment analysis using MindsDB, OpenAI, and SQL. We'll leverage MindsDB's ability to integrate with OpenAI's powerful language models to analyze text data directly within your database.

Prerequisites

Before you begin, ensure you have the following:

• MindsDB installed locally (via Docker or Docker Desktop).
• An OpenAI API key.

Tutorial

1. Connect to your Database: First, connect MindsDB to your MySQL database. Replace placeholders with your actual credentials.

CREATE DATABASE mysql_demo_db WITH ENGINE = 'mysql', PARAMETERS = { "user": "your_username", "password": "your_password", "host": "your_host", "port": "3306", "database": "your_database" };

2. Create an OpenAI Engine: Create an ML engine to manage your OpenAI integration, providing your API key.

CREATE ML_ENGINE openai_engine FROM openai USING openai_api_key = 'your-openai-api-key';

3. Create the Sentiment Analysis Model: Create a model to predict sentiment from text data. This uses a prompt template to structure the input for OpenAI.

CREATE MODEL sentiment_classifier_model PREDICT sentiment USING engine = 'openai_engine', prompt_template = 'describe the sentiment of the reviews strictly as "positive", "neutral", or "negative". "I love the product":positive "It is a scam":negative "{{review}}.":';

4. Check Model Status: Check the model creation status.

DESCRIBE sentiment_classifier_model;

5. Query the Model: Once complete, query the model. You can either provide individual reviews:

SELECT review, sentiment FROM sentiment_classifier_model WHERE review = 'It is ok.';

or join it with your data for batch predictions:

SELECT input.review, output.sentiment FROM mysql_demo_db.amazon_reviews AS input JOIN sentiment_classifier_model AS output LIMIT 3;

Leverage NLP Capabilities with MindsDB

MindsDB simplifies the integration of OpenAI's NLP capabilities into your database workflow. You can perform various NLP tasks with minimal SQL commands, saving time and resources compared to traditional ML development.

What's Next?

Experiment with different prompt templates and data sources to refine your sentiment analysis. Explore other MindsDB integrations and features to expand your AI capabilities.