sentimentide

Sentimentide is a proposed unit for quantifying sentiment in text and speech within affective computing. It is intended to capture the basic polarity of a sentiment expression together with a gradable intensity, allowing finer-grained analysis than a single positive/negative label.

Formally, a sentimentide is represented as a tuple (p, i), where p denotes polarity (positive, neutral, negative) and i denotes intensity on a bounded scale (for example 0 to 1). Some formulations also include a secondary dimension such as confidence or subjectivity.

Extraction methods combine lexical resources, machine learning, and contextual features. The intensity component can be derived from modifiers (very, somewhat), syntactic structure, discourse context, and cross-lingual signals. In practice, sentimentide assignments can be produced by rule-based heuristics or supervised models trained on annotated corpora.

Examples include: "extremely happy" → (positive, high), "mildly annoyed" → (negative, low-to-medium), "neutral statement" → (neutral, very low or zero).

Applications include more nuanced sentiment classification, sentiment-aware generation, and cross-domain or multilingual comparisons where consistent gradation is required.

Status: The term sentimentide is not widely standardized and remains primarily theoretical or experimental. Many NLP systems instead rely on scalar scores, probabilistic polarity, or dimensional models such as valence and arousal. Critics note challenges in cross-language transfer and annotation consistency.

See also: sentiment analysis, affective computing, emotion measurement, valence-arousal models.