infer_sentiment

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.

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Usage

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

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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").

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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).