Evaluating Prompt Engineering Strategies for Sentiment Control in AI-Generated Texts

• URL: http://arxiv.org/abs/2602.06692v1
• Date: Fri, 06 Feb 2026 13:22:40 GMT
• Title: Evaluating Prompt Engineering Strategies for Sentiment Control in AI-Generated Texts
• Abstract summary: This study investigates the potential of prompt engineering for controlling sentiment in AI-generated text.<n>Using Ekman's six basic emotions, we examine various prompting techniques, including Zero-Shot and Chain-of-Thought prompting.<n>Results indicate that prompt engineering effectively steers emotions in AI-generated texts, offering a practical and cost-effective alternative to fine-tuning.
• Score: 0.05371337604556311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The groundbreaking capabilities of Large Language Models (LLMs) offer new opportunities for enhancing human-computer interaction through emotion-adaptive Artificial Intelligence (AI). However, deliberately controlling the sentiment in these systems remains challenging. The present study investigates the potential of prompt engineering for controlling sentiment in LLM-generated text, providing a resource-sensitive and accessible alternative to existing methods. Using Ekman's six basic emotions (e.g., joy, disgust), we examine various prompting techniques, including Zero-Shot and Chain-of-Thought prompting using gpt-3.5-turbo, and compare it to fine-tuning. Our results indicate that prompt engineering effectively steers emotions in AI-generated texts, offering a practical and cost-effective alternative to fine-tuning, especially in data-constrained settings. In this regard, Few-Shot prompting with human-written examples was the most effective among other techniques, likely due to the additional task-specific guidance. The findings contribute valuable insights towards developing emotion-adaptive AI systems.

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