extract_text_features
Parse and process texts to extract multiple features at once.
Essentially combines all of the following steps into one:
embed_textextract_emojiextract_entitiesextract_hashtagsextract_keywordsextract_mentionsinfer_sentimenttokenize
Usage
The following shows how the step can be used in a recipe.Examples
Examples
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").
Inputs
Inputs
A text column to extract n-grams from.
An (optional) column identifying the languages of the corresponding texts. It is used to identify the correct model (spaCy)
to use for each text. If the dataset doesn’t contain such a column yet, it can be created using the
infer_language step.
Ideally, languages should be expressed as two-letter
ISO 639-1 language codes, such as “en”, “es” or “de” for
English, Spanish or German respectively. We also detect fully spelled out names such as “english”, “German”, “allemande”
etc., but it is not guaranteed that we will recognize all possible spellings correctly always, so ISO codes should be
preferred.Alternatively, if all texts are in the same language, it can be identified with the lang parameter instead.Outputs
Outputs
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).
Parameters
Parameters
This step doesn’t expect any specific parameters.