Extract text features

NLP • text

Parse and process texts to extract multiple features at once.

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

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The following are the step's expected inputs and outputs and their specific types.

Step signature

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]
)

where the object {"param": value} is optional in most cases and if present may contain any of the parameters described in the corresponding section below.

Inputs

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text: column:text

A text column to extract n-grams from.

---

*lang: 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

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Sentiment: column:number

---

Embedding: column:list[number]

---

Hashtags: column:list[category]

---

Mentions: column:list[category]

---

Keywords: column:list[category]

---

Tokens: column:list[category]

---

Emoji: column:list[category]

---

People: column:list[category]

---

Groups: column:list[category]

---

Organizatons: column:list[category]

---

GPEs: column:list[category]

---

Locations: column:list[category]

---

Products: column:list[category]

---

Events: column:list[category]

---

Money: column:list[category]

Parameters

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