infer_sentiment(text: text, *lang: category, {
    "param": value,
    ...
}) -> (sentiment: number)

Polarity is measured on the normalized scale [-1, 1]. The method used here is rather naïve. It simply looks up each word in the text in a “polarity lexicon”, which assigns each emotionally charged word a numeric score. The individual scores are then simply averaged across the whole text. This will hence not account for contexts involving irony, sarcasm, or even simple negations.

infer_sentiment(text: text, *lang: category, {
    "param": value,
    ...
}) -> (sentiment: number)
​
extended_language_support
boolean

Whether to enable support for additional languages. By default, Arabic (“ar”), Catalan (“ca”), Basque (“eu”), and Turkish (“tu”) are not enabled, since they’re supported only by a different class of language models (stanfordNLP’s Stanza) that is much slower than the rest. This parameter can be used to enable them.

​
min_language_freq
[number, integer]
default:"0.02"

Minimum number (or proportion) of texts to include a language in processing. Any texts in a language with fewer documents than these will be ignored. Can be useful to speed up processing when there is noise in the input languages, and when ignoring languages with a small number of documents only is acceptable. Values smaller than 1 will be interpreted as a proportion of all texts, and values greater than or equal to 1 as an absolute number of documents.

​
language
[string, null]

The language of inputs texts. If all texts are in the same language, it can be specified here instead of passing it as an input column. The language will be used to identify the correct spaCy model to parse and analyze the texts. For allowed values, see the comment regarding the lang column above.

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