Azure AI Fundamentals - Text Analytics Exercise

This project demonstrates how to use Azure AI's Text Analytics API to perform natural language processing tasks such as language detection, sentiment analysis, key phrase extraction, and entity recognition on text documents.

This is part of a hands-on exercise to prepare for the Microsoft AI-900: Azure AI Fundamentals certification.

Project Structure

.
├── reviews/              # Folder containing text files to be analyzed
├── .env                  # Environment file with Azure AI credentials
├── analyze_reviews.py    # Main script for analyzing text documents
├── README.md             # Project documentation

Features

This script uses Azure's TextAnalyticsClient to perform the following tasks on each text file in the reviews/ folder:

• Detect Language
• Analyze Sentiment
• Extract Key Phrases
• Recognize Named Entities
• Recognize Linked Entities

Prerequisites

• Python 3.7+
• Azure subscription with Azure Cognitive Services enabled
• Provisioned Text Analytics resource in Azure

Installation

1. Clone the repository or copy the script.
2. Install dependencies:

pip install azure-ai-textanalytics python-dotenv

3. Create a .env file in the project root:

azureAI_Endpoint=https://<your-resource-name>.cognitiveservices.azure.com/
azureKey=<your-text-analytics-key>

4. Add .txt files to the reviews/ folder for analysis.

Usage

Run the script:

python analyze_reviews.py

Each file's results will be printed to the console, including detected language, sentiment, key phrases, named entities, and linked entities.

Learn More

• AI-900 Certification Guide