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
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.
Resources to Get Started
![Read the Tutorial]()
Read the Tutorial
Read this deep dive tutorial to learn how to build an end-to-end deep learning pipeline for sentiment analysis.
![Run the Notebook]()
Run the Notebook
Get started with sentiment analysis using this open-source Ludwig notebook including sample code and data.
![Watch the Webinar]()
Watch the Webinar
Watch this webinar to learn more about Predibase, Ludwig and the steps it takes to build a multimodal sentiment analysis model.
