Machine Learning Use Case

Customer Sentiment Analysis

Sentiment analysis is critical to understanding how your customers feel about your products or services. Learn how to build an end-to-end machine learning pipeline to automatically classify customers as positive or negative in minutes with Predibase.

The Predibase Solution

Get to know your customers better with ML-powered sentiment analysis

  • Rapidly preprocess tabular and text features and evaluate importance
  • Finely tune model parameters such as using pre-trained encoders like Bert with little-to-no-code
  • Automate the classification of customer reviews as positive or negative to improve decision making

Multimodal Data

Customer Demographics
Customer Demographics
Purchase Histories
Purchase Histories
Customer Reviews
Customer Reviews
Chat Logs
Chat Logs
Product Information
Product Information
Store Locations
Store Locations
Customer Sentiment Analysis

Business Value

Improve and automate 
customer service
Improve and automate 
customer service
Enhance product design decision
Enhance product design decision
Identify underserved customer segments
Identify underserved customer segments

Train your first sentiment analysis model in < 20 lines of a config file

At the core of Predibase is Ludwig, an open-source declarative ML framework, that automates complex model development with a simple configuration file. Predibase builds on these capabilities with a collaborative, easy-to-use, and fully managed ML platform in the cloud.

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Resources to Get Started

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