This paper presents the Sentiment Syndicate team’s submission to SemEval-2026 Task 6, Subtask 1 (CLARITY: Unmasking Political Question Evasions), which focuses on classifying the clarity level of political question–answer interactions. We investigate three modeling strategies: (1) fine-tuning a RoBERTa-based classifier, (2) reformulating the task as a Natural Language Inference (NLI) problem, and (3) leveraging large language models (LLMs) for classification. All approaches are evaluated using macro F1-score on the official dataset. Experimental results demonstrate that the NLI based formulation outperforms the other strategies, highlighting the effectiveness of modeling semantic alignment between questions and answers. Our best-performing system achieves an F1-score of 0.67 on the test set.