With the Sherpa.ai Content Analysis AI API, you can determine the sentiment load of a paragraph or text document. This AI model combines NLP and ML techniques to assign weighted sentiment scores to a text document. This way, the sentences of a text document and the text document as a whole can be characterized by their polarity, a measure that summarizes the overall emotional charge:
A text sentiment metric summarizing the overall emotional charge of a sentence or a document. The polarity of a text sentence or document takes a value in the range [-1, +1]. Large negative values indicate a strong negative emotional charge, and conversely, large positive values indicate a strong positive emotional charge. Values close to zero indicate an overall neutral emotional charge.
Polarity is a quantitative metric, resulting from the combination of the following two emotional characteristics:
- Valence: the emotional value, how enjoyable a stimulus is; ranges from pleasant / happy (+1) to unpleasant / unhappy (-1). The valence is the base variable for estimating the overall polarity charge (positive, neutral or negative).
- Arousal: the psychological state of being awoken. It ranges from calm (0.0) to excited (1.0). The arousal works as a catalyzer of the base stimulus provided by the valence, reinforcing the overall polarity charge when the arousal is high and suppressing it when the arousal is low.
The Valence / Arousal relationship could be displayed in a circumplex model as a function of the valence’s emotional charge and the arousal’s intensity. Words in the lexicons could be displayed in a two-dimensional affective space. The following figures depict some examples:
- Enter text in the box
- Click “Analyze”
- Evaluate the total scores and the scores for each sentence
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