extract_text_features

Essentially combines all of the following steps into one:

embed_text
extract_emoji
extract_entities
extract_hashtags
extract_keywords
extract_mentions
infer_sentiment
tokenize

Note that the step does not currently allow for detailed configuration of each of the extracted features. To do that, use any or all of the individual steps above.

Usage

The following examples show how the step can be used in a recipe.

Examples

Example 1
Example 2
Signature

Extract all text features with automatic language detection

extract_text_features(ds.text) => (ds.sentiment, ds.embedding, ds.hashtags, ds.mentions, ds.keywords, ds.tokens, ds.emoji, ds.people, ds.groups, ds.organizations, ds.gpes, ds.locations, ds.products, ds.events, ds.money)

Extract text features providing a language column

extract_text_features(ds.text, ds.language) => (ds.sentiment, ds.embedding, ds.hashtags, ds.mentions, ds.keywords, ds.tokens, ds.emoji, ds.people, ds.groups, ds.organizations, ds.gpes, ds.locations, ds.products, ds.events, ds.money)

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.

extract_text_features(text: text, *lang: category, {
    "param": value,
    ...
}) -> (
	Sentiment: number,
	Embedding: list[number],
	Hashtags: list[category],
	Mentions: list[category],
	Keywords: list[category],
	Tokens: list[category],
	Emoji: list[category],
	People: list[category],
	Groups: list[category],
	Organizatons: list[category],
	GPEs: list[category],
	Locations: list[category],
	Products: list[category],
	Events: list[category],
	Money: list[category]
)

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

column[text]

required

A text column to extract n-grams from.

column[category]

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

column[number]

required

column[list[number]]

required

column[list[category]]

required

column[list[category]]

required

column[list[category]]

required

column[list[category]]

required

column[list[category]]

required

column[list[category]]

required

column[list[category]]

required

column[list[category]]

required

column[list[category]]

required

column[list[category]]

required

column[list[category]]

required

column[list[category]]

required

column[list[category]]

required

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

This step doesn’t expect any specific parameters.

Analyse

Report

Prepare

Usage

Inputs & Outputs

Configuration

​Usage

​Inputs & Outputs

​Configuration

Usage

Inputs & Outputs

Configuration