| Abstract:The powerful language capabilities and ease of use of generative AI have significantly influenced the scope, methods, perspectives, and depth of textual data utilization. It effectively addresses issues in existing text analysis methods, such as low accuracy, high complexity, substantial costs, and insufficient utilization of textual data, thereby enhancing the breadth and depth of research. In the era of generative AI, text analyses may exhibit three potential trends: generative AI becoming a standard tool for handling diverse text-based tasks, generative AI providing intelligent assistance, the “end-to-end” of generative AI becoming a new research process. Prompts are critical for generative AI to achieve high-quality text analyses, optimization can be achieved through providing examples, applying chain-of-thought, and iterative testing. Additionally, when using generative AI, special attention must be paid to avoiding “hallucinations” and protecting data privacy. |
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