Task Complexity Matters: An Empirical Study of Reasoning in LLMs for Sentiment Analysis

A comprehensive study of 504 AI model configurations reveals that reasoning capabilities in large language models are highly task-dependent, with simple tasks like binary classification actually degrading by up to 19.9 percentage points while complex 27-class emotion recognition improves by up to 16.0 points. The research challenges the assumption that reasoning universally improves AI performance across all language tasks.

• →Reasoning effectiveness in LLMs is strongly dependent on task complexity, contradicting assumptions of universal performance improvement.
• →Simple binary classification tasks degrade significantly with reasoning (up to -19.9 F1 points) while complex emotion recognition benefits (+16.0 points).
• →Distilled reasoning variants consistently underperform base models by 3-18 percentage points on simpler tasks.
• →Base models dominate efficiency-performance trade-offs, with reasoning architectures carrying 2.1x-54x computational overhead.
• →Over-deliberation through reasoning causes systematic degradation in simpler language processing tasks.