label_texts_containing
Categorize texts containing specific keywords with custom labels.
Assigns each text to one or more categories. Each category is defined by a list of keywords a text must include or exclude to be labelled accordingly. In addition, each category may specify whether a keyword must be matched explicitly, ignoring its case (lower, upper) etc. See parameters below for further details.
Usage
The following example shows how the step can be used in a recipe.
The following defines the keywords to be included or exluded for each of three categories, labelled “journalist”, “business” and “CEO”. Note how in the case of “CEO” we’re looking for occurrences of the spelling with capitals only.
The following defines the keywords to be included or exluded for each of three categories, labelled “journalist”, “business” and “CEO”. Note how in the case of “CEO” we’re looking for occurrences of the spelling with capitals only.
General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.
Inputs & Outputs
The following are the inputs expected by the step and the outputs it produces. These are generally
columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced
by name e.g. "churn-clf").
Configuration
The following parameters can be used to configure the behaviour of the step by including them in
a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).
One or more named text categories. Each parameter should be a key indicating the name/label to show for a specific text category, and should have an object as value specifying the terms a text must or must not contain for that particular label to apply. Also see examples above.