<![CDATA[Sentiment analysis is a core task in natural language processing with broad ap plications in social media monitoring, customer feedback mining, and market research. Although pre-trained language models (e.g., BERT) achieve strong performance, they typically rely on task-specific fine-tuning and substantial la beled data. Recent large language models (LLMs) enable a different paradigm via in-context learning. This paper presents a systematic empirical study investi gating chain-of-thought sentiment (CoT-Sent), a prompting framework that uses structured CoT reasoning to improve classification accuracy. We evaluate CoT Sent on four benchmark datasets in English and Chinese, comparing multiple representative LLMs (GPT-4, Claude-3, Gemini, Qwen-2.5) under zero-shot set tings. Across datasets, CoT-Sent improves average accuracy by 2.5% over zero shot baselines. Crucially, unlike prior work which provides a broad performance overview without analyzing deployment costs or multi-language generalization, we focus on the cost-latency-accuracy trade-offs, and demonstrate CoT-Sent’s superior cross-lingual transfer (English-to-Chinese) with detailed cost analysis. We provide a comprehensive three-dimensional analysis of accuracy, cost, and latency, offering actionable deployment strategies for resource-constrained environments.]]>
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