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¶
The following are the step's expected inputs and outputs and their specific types.
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¶
text: column:text
lang: column:category
Outputs¶
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]