Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
post
page
Filter by Categories
Data Scraping
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
post
page
Filter by Categories
Data Scraping

Exact match

An exact match is an identical match to a concept you select. For example, if you analyzed the concept “app,” exact matches might include the concept “app,” “App”, or “apps” but not closely related concepts like “tablet” or “download.”

Association score

Association scores measure how related two concepts are on a scale of -1.00 to 1.00. These numbers represent the weakest and the strongest possible relationships. A concept has an association score of 1.00 with itself. A score of 0 indicates that two concepts are only as associated with one another in a project as they […]

Metadata

Any additional data that accompanies the unstructured text on a document. Metadata could include information like date, number, star rating, or demographic. You can also include qualitative labels like “male,” or “Massachusetts.” After uploading metadata, sort and filter your data to drill down to specific insights

Filter

You can use metadata to filter documents in Daylight and view specific subsets of documents. For example, you might create a filter based on location and age to see only documents from 45- to 65-year-olds from Massachusetts or Rhode Island.

Saved concept

Save a concept to access it easily in Daylight. Once you save a concept, associate a color with it in the Galaxy feature and view its exact and conceptual matches. You can rename a saved concept, though renaming a concept won’t change the way it interacts with your project.

Conceptual match

A conceptual match is a close but not identical match to a concept you select. Since using only exact matches might skip important information, Luminoso also provides a powerful conceptual matching tool. For example, if you select the concept “delicious,” conceptual matches might include “yummy” and “tasty.”

Prevalence

If a word or a phrase appears more frequently in a project than it does in the language as a whole, it is prevalent. For example, in a consumer electronics project, the word “wifi” may occur 1000 times more frequently than it does across the English language. This indicates the word’s prevalence in the project.