Introduction

  • 3 minutes

In natural language processing (NLP), sentiment analysis and opinion mining are pivotal in extracting valuable insights from textual data. Learning these concepts, their differences, and their benefits is essential for building intelligent applications that process written language and provide insights into discovering how customers truly feel about a brand, product, or topic. Sentiment analysis provides insights into market trends, competitor performance, and consumer preferences. Understanding sentiment helps businesses adapt their strategies accordingly.

Scenario: Analyzing the sentiment of user property reviews

You're a developer for Margie's Travel, a company whose web and mobile apps connect travelers looking for lodging accommodations with homeowners and property managers willing to rent out their properties. Azure Database for PostgreSQL flexible server database backs these apps. One of the app's features allows renters to submit reviews of the properties they rented. These reviews enable other customers to determine the quality of accommodations and the helpfulness of hosts. You're asked to use Azure AI Services and the azure_ai extension to analyze the sentiment of reviews so descriptive labels can be applied as filters within the apps.

Sentiment analysis: Understanding the big picture

Sentiment analysis is like having an emotional radar for text. It helps you gauge the feelings or emotional tone expressed in written content. Whether it's a product review, a social media post, or customer feedback, sentiment analysis reveals whether the sentiment is positive, negative, or neutral. This capability provides insights into how users perceive your brand, product, or service.

Opinion mining: Going beyond sentiment

Opinion mining (also known as aspect-based sentiment analysis) takes sentiment analysis to the next level. It is like dissecting opinions under a microscope. Instead of overall sentiment, opinion mining zooms in on specific aspects of the text. For instance, it can tell you if users love the spacious rooms but find the surroundings noisy. This capability provides a deeper understanding of more nuanced sentiments associated with different attributes and is ideal for conducting fine-grained analysis.

Analyze sentiment with Azure AI Services

The Azure AI Language service, part of Azure AI Services, allows you to analyze sentiment and mine opinions from textual data. Incorporating sentiment analysis capabilities into your applications can be accomplished seamlessly using the azure_ai extension for the Azure Database for PostgreSQL flexible server.

Learning objectives

The module explores the sentiment analysis and opinion mining capabilities of the Azure AI Language service and how the azure_ai extension can be used to integrate sentiment analysis directly into your PostgreSQL databases. In this module, you:

  • Explore the fundamentals of sentiment analysis and how it can be applied to gain insights into user sentiments and emotions.
  • Describe opinion mining techniques to identify sentiments related to specific attributes.
  • Apply sentiment analysis to user reviews in a PostgreSQL database using the azure_ai extension.

By the end of this module, you're equipped to build intelligent applications that understand sentiments and opinions directly within your database.