In this blog post, we’ll follow a step-by-step guide to building a review analysis pipeline. We’ll leverage the Databricks Marketplace to import sample data, use ai_query() to create a pipeline of reviews and serve that analysis in an AI/BI dashboard. The final result is an interactive way for users to understand customer opinions from product reviews.
Analyzing data from free-form text reviews can provide critical insights into customer feedback. What do customers think about your business? What particular aspects can be improved?
Traditionally, understanding large volumes of unstructured text data like user reviews involves a significant investment. You really need ML engineers to train and deploy classification and/or named entity recognition models that are purpose-built for each task. With AI Functions, however, Databricks is making it possible for anyone comfortable working in SQL to get answers to these questions. No bespoke modeling is required. It’s as simple as writing a few lines of SQL.
In this blog post, we’ll follow a step-by-step guide for a SQL analyst to understand trends in freeform text reviews. We’ll leverage the Databricks Marketplace to import sample data, then use ai_query() to create a pipeline of reviews. We’ll then display and share that analysis using an AI/BI dashboard. The final result is an interactive way for users to understand customer opinions from product reviews.
Let’s walk through the process of mining opinions in Databricks SQL.
For this example, we’ll use a sample Amazon reviews dataset from BrightData, one of the partners on the Databricks Marketplace. To access this data, navigate to the Databricks Marketplace via the left-hand navigation menu.
Databricks Marketplace is an open marketplace for all your data, analytics and AI, powered by the open source Delta Sharing standard. The Databricks Marketplace expands your opportunity to deliver innovation and advance all your analytics and AI initiatives. You can leverage these assets directly within your Databricks environment.