Towards Explainable Khmer Polarity Classification

TL;DR

This paper introduces an explainable Khmer polarity classifier using a fine-tuned Qwen-3 model that predicts sentiment labels and provides rationales, supported by a new Khmer polarity dataset.

Contribution

It presents a novel explainable sentiment classification approach for Khmer using instruction-based reasoning and releases a new dataset and models.

Findings

The model accurately predicts polarity labels.

It provides self-explanations supporting its predictions.

The dataset is publicly available for further research.

Abstract

Khmer polarity classification is a fundamental natural language processing task that assigns a positive, negative, or neutral label to a given Khmer text input. Existing Khmer models typically predict the label without explaining the rationale behind the prediction. This paper proposes an explainable Khmer polarity classifier by fine-tuning an instruction-based reasoning Qwen-3 model. The notion of explainability in this paper is limited to self-explanations, which the model uses to rationalize its predictions. Experimental results show that the fine-tuned model not only predicts labels accurately but also provides reasoning by identifying polarity-related keywords or phrases to support its predictions. In addition, we contribute a new Khmer polarity dataset consisting of short- to medium-length casual, romanized, and mixed-code Khmer expressions. This dataset was constructed using both heuristic rules and human curation and is publicly available through a gated Hugging Face repository (rinabuoy/khmerpolarity_nonreasoning). The fine-tuned Qwen-3 models are also made available in the same Hugging Face account.