Controlling the behavior of large language models (LLMs) at inference time is essential for aligning outputs with human abilities and safety requirements. Activation steering provides a lightweight alternative to prompt engineering and fine-tuning by directly modifying internal activations to guide generation. This research advances the literature in three significant directions. First, while previous work demonstrated the technical feasibility of steering emotional tone using automated classifiers, this paper presents the first human evaluation of activation steering concerning the emotional tone of LLM outputs, collecting over 7,000 crowd-sourced ratings from 190 participants via Prolific (<inline-formula> <tex-math notation="LaTeX">$n=190$ </tex-math></inline-formula>). These ratings assess both perceived emotional intensity and overall text quality. Second, we find strong alignment between human and model-based quality ratings (mean <inline-formula> <tex-math notation="LaTeX">$r=0.776$ </tex-math></inline-formula>, range 0.157–0.985), indicating automatic scoring can proxy perceived quality. Moderate steering strengths (<inline-formula> <tex-math notation="LaTeX">$\lambda \approx 0.15$ </tex-math></inline-formula>) reliably amplify target emotions while preserving comprehensibility, with the strongest effects for disgust (<inline-formula> <tex-math notation="LaTeX">$\eta _{p}^{2} = 0.616$ </tex-math></inline-formula>) and fear (<inline-formula> <tex-math notation="LaTeX">$\eta _{p}^{2} = 0.540$ </tex-math></inline-formula>), and minimal effects for surprise (<inline-formula> <tex-math notation="LaTeX">$\eta _{p}^{2} = 0.042$ </tex-math></inline-formula>). Finally, upgrading from Alpaca to LlaMA-3 yielded more consistent steering with significant effects across emotions and strengths (all <inline-formula> <tex-math notation="LaTeX">$p \lt 0.001$ </tex-math></inline-formula>). Inter-rater reliability was high (ICC = 0.71–0.87), underscoring the robustness of the findings. These findings support activation-based control as a scalable method for steering LLM behavior across affective dimensions.