Quantum modules improve text emotion classification with targeted specialists
EmotiQ: A Quantum Residual Specialist Framework for Text Emotion Classification
Computation and Language
Summary
Text emotion classification is hard because emotions can be expressed with subtle and overlapping language. The authors present EmotiQ, a system that uses quantum computing components alongside regular text classifiers to focus on tricky emotion distinctions. These quantum modules act like specialists that only step in when they are confident, helping improve overall emotion recognition. Tests across six datasets show small but consistent improvements when these quantum specialists assist traditional methods.
What this means in practice
- •For sentiment analysis developers: Integrate quantum residual modules to enhance emotion detection accuracy in text classification pipelines where subtle emotional nuances are critical.
- •For quantum computing engineers: Deploy quantum feature maps as focused specialists to improve specific linguistic classification tasks guided by classical model performance.
Authors
Carlton Kigonya, Tanaka Chirara, Yunting Yin
Abstract
Emotion classification from text is challenging because many affective states are distinguished by subtle linguistic signals and semantically overlapping boundaries. We investigate whether compact quantum modules can serve as targeted residual specialists that assist traditional classifiers on difficult emotion decisions. We introduce EmotiQ, a residual specialist framework for text emotion classification using a frozen transformer classifier as the primary decision maker, identifies challenging emotion boundaries from validation behavior, and trains boundary specialists, including quantum encodings, data re-uploading specialists, prototype and fidelity specialists, and kernel-based specialists. Specialists are permitted to intervene only when they satisfy validation-based admission criteria. We evaluate the framework across six text emotion classification datasets and establish traditional model baselines. Experimental results show that the admitted EmotiQ policy improves mean macro-F1 metrics over the baseline, and achieves positive gains on five of six datasets. Among the quantum components, ZZ interaction feature maps are the most consistently effective, yielding a mean macro-F1 improvement of +0.0030 and positive gains on four datasets. These findings suggest that current quantum NLP methods can be deployed as residual specialists that provide localized improvements under strong classical supervision.