The following figure compares two popular affective models that can guide the automated classification of human emotions.
A common distinction is the intensity of the expressed emotion. Many of these models also define sub-categories for a more nuanced representation.
They provide more fine-grained classifications into emotional categories. More complex affective models consider the work of psychologists. It uses sentiment lexicons together with artificial intelligence-based machine learning methods to determine the polarity of sentences and documents. Sentiment analysis, for example, classifies content streams into positive and negative expressions. Affective ComputingĪffective models for opinion mining differ in their goals and complexity.
Translated into color coding, these dimensions enrich visualizations such as tag clouds and geographic maps. webLyzard detects emotions with affective computing algorithms that classify and sort search results along multiple emotional dimensions such as Joy, Trust, Anger and Fear. Emotion detection algorithms for news and social media coverage help to investigate trends and differing perceptions of various stakeholder groups.
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