> ## Documentation Index
> Fetch the complete documentation index at: https://docs.graphext.com/llms.txt
> Use this file to discover all available pages before exploring further.

# infer_sentiment

> Parse text and calculate the overall positive or negative sentiment polarity. 

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.

## Usage

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

<Accordion title="Examples" icon="code" defaultOpen="true">
  <Tabs>
    <Tab title="Example 1">
      To detect the sentiment for languages supported by default, use:

      ```stan theme={null}
      infer_sentiment(ds.text, ds.lang) -> (ds.sentiment)
      ```
    </Tab>

    <Tab title="Example 2">
      To only process those languages used in at least 1% of the input texts:

      ```stan theme={null}
      infer_sentiment(ds.text, ds.lang, {"min_lang_docs": 0.01}) -> (ds.sentiment)
      ```
    </Tab>

    <Tab title="Signature">
      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.

      ```stan theme={null}
      infer_sentiment(text: text, *lang: category, {
          "param": value,
          ...
      }) -> (sentiment: number)
      ```
    </Tab>
  </Tabs>
</Accordion>

## 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"`).

<Accordion title="Inputs" icon="right-to-bracket">
  <ParamField path="text" type="column[text]" required>
    A text column to infer sentiment polarities for.
  </ParamField>

  <ParamField path="*lang" type="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](https://en.wikipedia.org/wiki/List_of_ISO_639-1_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 `language` *parameter* instead.
  </ParamField>
</Accordion>

<Accordion title="Outputs" icon="right-from-bracket">
  <ParamField path="sentiment" type="column[number]" required>
    A column containing the overall sentiment polarity for each input text.
  </ParamField>
</Accordion>

## 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)`.

<Accordion title="Parameters" defaultOpen="true" icon="sliders">
  <ParamField path="extended_language_support" type="boolean" default="false">
    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.
  </ParamField>

  <ParamField path="min_language_freq" type="[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.

    <Accordion title="Options">
      <Tabs>
        <Tab title="number">
          <ParamField path="{_}" type="number">
            number.

            Values must be in the following range:

            ```javascript theme={null}
            0 < {_} < 1
            ```
          </ParamField>
        </Tab>

        <Tab title="integer">
          <ParamField path="{_}" type="integer">
            integer.

            Values must be in the following range:

            ```javascript theme={null}
            1 ≤ {_} < inf
            ```
          </ParamField>
        </Tab>
      </Tabs>
    </Accordion>
  </ParamField>

  <ParamField path="language" type="[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.
  </ParamField>
</Accordion>