markedssentiment

Markedssentiment is a framework in natural language processing designed to enhance sentiment analysis by pairing traditional polarity labels with an auxiliary set of markers that encode contextual and pragmatic modifiers. This approach seeks to capture not only the overall sentiment of a text but also how various linguistic cues influence its interpretation.

The concept appears in recent academic and industry literature as a way to represent more nuanced sentiment across domains. The term markedssentiment (sometimes written with different capitalization) denotes the combination of a base sentiment label with discrete markers that indicate features such as negation, intensity, hedging, sarcasm cues, and discourse signals. It is used in multiple implementations and can vary in exact encoding or naming.

In typical implementations, a token-level or span-level representation assigns to each unit a base polarity (positive, negative, neutral) and a vector of marks capturing negation scope, emphasis, sarcasm indicators, and discourse relations. This can be represented as sequences of (token, polarity, marks) and integrated into supervised models, rule-based systems, or attention-based architectures. For example, the sentence "I really don't like this at all" might have a negative base polarity with marks indicating negation over "don't," emphasis on "really," and a heightened intensity through "at all."

Applications of markedssentiment include sentiment classification, aspect-based sentiment analysis, sarcasm and irony detection, and broader opinion mining in social media, product reviews, and customer feedback. Advantages include improved handling of negation, intensity, and discourse structure, which can boost accuracy on challenging texts. Limitations involve the need for specialized annotations, potential data sparsity for less common marks, added model complexity, and cross-domain generalization challenges. Related topics include sentiment analysis, affective computing, sarcasm detection, and emotion recognition.