Psycholinguistic analyses in software engineering text: A systematic mapping study

A deeper understanding of human factors in software engineering (SE) is essential for improving team collaboration, decision-making, and productivity. Communication channels like code reviews and chats provide insights into developers’ psychological and emotional states. While large language models excel at text analysis, they often lack transparency and precision. Psycholinguistic tools like Linguistic Inquiry and Word Count (LIWC) offer clearer, interpretable insights into cognitive and emotional processes exhibited in text. Despite its wide use in SE research, no comprehensive mapping study of LIWC’s use has been conducted. We examine the importance of psycholinguistic tools, particularly LIWC, and provide a thorough analysis of its current and potential future applications in SE research. We conducted a systematic mapping study of six prominent databases, identifying 43 SE-related papers using LIWC. Our analysis focuses on five research questions: RQ1. How was LIWC employed in SE studies, and for what purposes?, RQ2. What datasets were analyzed using LIWC?, RQ3: What Behavioral Software Engineering (BSE) concepts were studied using LIWC? RQ4: How often has LIWC been evaluated in SE research?, RQ5: What concerns were raised about adopting LIWC in SE? Our findings reveal a wide range of applications, including analyzing team communication to detect developer emotions and personality, developing ML models to predict deleted Stack Overflow posts, and more recently comparing AI-generated and human-written text. LIWC has been primarily used with data from project management platforms (e.g., GitHub) and Q&A forums (e.g., Stack Overflow). Key BSE concepts include Communication , Organizational Climate , and Positive Psychology . 26 of 43 papers did not formally evaluate LIWC. Concerns were raised about some limitations, including difficulty handling SE-specific vocabulary. We highlight the potential of psycholinguistic tools and their limitations, and present new use cases for advancing research on human factors in SE (e.g., bias in human-LLM conversations).