Generating User Personas for Eliciting Requirements Using Online News Data
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Background: In software development, creating user personas remains challenging despite their recognized value. Cost, adaptability, and data scarcity present obstacles in designing these critical personas. A new perspective and process innovation for generating user personas is essential to overcome this hurdle.
Objective: This study introduces a method for extracting user persona attributes, including names, occupations, workplaces, and goals.
Methods: A framework for extracting user persona information from online news sources is created. Our method employs a comprehensive SpaCy processing pipeline, incorporating NER, SpaCy rule-based matching, and phrase matching.
Results: The evaluation results showcase promising performance metrics, with an average recall value of 0.700, precision of 0.402, and F1-score of 0.506.
Conclusion: This study demonstrates the feasibility of extracting user persona attributes from online news data. Future research could focus on enhancing the method’s performance, investigating its effectiveness in creating relationships, and ensuring that the generated user personas accurately reflect the news text data.
Keywords: Process innovation, Natural Language Processing, Online News, Software Development, User Persona
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