Leveraging Sentiment Analysis and Reporting for Re-designing Business Processes Using Large Language Models: A SentiProMo Case Study in Airline Check-In Processes

In today’s competitive landscape, optimizing business processes is crucial for maintaining efficiency and customer satisfaction, particularly in the manufacturing sector. This paper presents SentiProMo, a self-developed tool that integrates sentiment analysis of collaborative comments with summarization features to enhance the design phase of business processes. Leveraging the capabilities of large language models (LLMs) such as Chat-GPT, this tool empowers managers with insightful reports for informed decision-making. To demonstrate the effectiveness of SentiProMo, a case study was conducted focusing on the check-in process at airports, a critical aspect of the airline industry. Real-world data from collaborative comments during the design phase of the check-in process were analyzed using sentiment analysis techniques. Additionally, summarization features were employed to generate concise and informative reports for management stakeholders. The results of the case study showcase the potential of LLM-powered tools like SentiProMo in streamlining business processes. By harnessing sentiment analysis, organizations can gain valuable insights into employee perceptions and identify areas for improvement. Moreover, the summarization capabilities facilitate the efficient communication of findings to management, enabling them to make informed decisions promptly. This research not only underscores the power of LLMs in enhancing business process design but also highlights the promising avenues for future applications. With its wide array of potential applications across various industries, SentiProMo represents a significant advancement in process optimization and management.