Formatting data
Now that you have an account on Daylight and access to a Workspace, the next step is to upload some text data to analyze. You can refer to the Using different data sources with Daylight page for some pointers on different types of text data to consider. You will also want to include some metadata to provide context for […]
Sentiment feature: Detect positive, negative, and neutral
Use Sentiment to learn about concepts’ association with types of emotion. Daylight uses a proprietary deep learning model to determine sentiment for all concepts in a project and assigns a label of positive, negative, or neutral, based on the concept’s context. Please use the percentage counts in this feature as a general indication.
Volume feature: Quantify concepts
Gain quick insight into concept representation across the dataset. The Volume feature automatically quantifies how frequently concepts are referenced in the project using the exact and conceptual matching identified by QuickLearn.
Start with the Highlights feature
The Highlights feature is a starting point in understanding the contents of your project. In this feature, orient yourself and start navigating the primary questions you can answer using Daylight: You’ll first view Daylight’s answers to these questions in Highlights, and then engage deeply with each data perspective as you move through the Volume, Sentiment, Drivers, and Galaxy features. What metadata do my documents have? — View the […]