The best keywords in domain-specific short transcripts are those with high specificity and technical importance, directly addressing product/service issues or solutions. Keywords with higher priority provide more accurate representations of conversations. Traditional keyword extraction methods like TF-IDF, YAKE, TextRank, and LDA are limited in handling short texts and domain-specific contexts, as they focus on frequency or general co-occurrence without considering technical priority. The challenge lies in determining keyword priority, which requires contextual understanding and adaptation to evolving domain-specific needs. Following Peffers ' design science methodology, this research introduces a novel approach to calculating keyword priority scores in short transcripts. The approach enhances keyword extraction and topic classification, demonstrating efficacy through experiments. Its adaptable nature makes it suitable for various domains, contributing methodologically and practically. Applications include in-house systems, drafting proposals, or outsourcing knowledge retrieval services, providing significant advancements in domain-specific keyword analysis.