Data-driven user personas in requirement engineering with NLP and behavior analysis

As technology rapidly evolves, software development faces growing complexity, requiring adaptation to dynamic user expectations. This study addresses a critical gap in the existing literature by integrating behavioral data and sentiment analysis into the user persona development process within the requirement engineering framework. The primary objective is to create more accurate and representative user personas that better guide software design and development. To achieve this, the research employs advanced Natural Language Processing (NLP) techniques to systematically analyze extensive behavioral and sentiment data collected from social media platforms. The integration process involves segmenting user data into behavioral patterns and emotional states, which are then synthesized to develop nuanced user personas. These personas are expected to significantly improve the accuracy of user requirements, leading to enhanced software performance, increased user satisfaction, and greater development efficiency. The target application area for this research is mobile telecommunications, where precise user understanding is critical. The results indicate that this approach not only refines the traditional persona method but also addresses the evolving needs of users more holistically. By advancing the methodology for user-centered design, this study contributes to the broader field of requirement engineering. Future research will validate and refine this approach across diverse domains, ensuring its adaptability and effectiveness in different contexts. This paper thus has the potential to make a significant impact on how user personas are developed and utilized in software engineering

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