기계번역에 관한 사용자 인식 및 경험 고찰: 번역 앱 후기의 텍스트 마이닝 분석을 토대로
A Text Mining Analysis on User Perceptions and Experiences of Machine Translation
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This study investigates user perceptions and experiences of machine translation (MT) through text mining analyses of user reviews, conceptualized as digital paratexts of translation, for Google Translate, Papago, and DeepL. Employing keyword extraction, sentiment analysis, and selective qualitative analysis on 4,913 reviews, this study identifies key factors influencing user engagement and technology acceptance, including translation quality, usability, and social influences. The findings indicate that users perceive Google Translate as beneficial for its extensive language coverage, Papago as effective for language learning due to its user-friendly tools, and DeepL as superior in accuracy and naturalness. Despite generally positive attitudes toward MT, users highlight critical areas for improvement, such as interface usability and limited language support. Moreover, the reviews reflect broader socio-cultural dynamics, illustrating how societal narratives shape MT adoption. This study underscores the complementary role of MT alongside human translation and offers practical insights for developers, practitioners, educators, and researchers. By exploring user-driven insights, this research advances understanding of the evolving landscape of MT and its integration into professional and educational contexts.
Abstract
This study investigates user perceptions and experiences of machine translation (MT) through text mining analyses of user reviews, conceptualized as digital paratexts of translation, for Google Translate, Papago, and DeepL. Employing keyword extraction, sentiment analysis, and selective qualitative analysis on 4,913 reviews, this study identifies key factors influencing user engagement and technology acceptance, including translation quality, usability, and social influences. The findings indicate that users perceive Google Translate as beneficial for its extensive language coverage, Papago as effective for language learning due to its user-friendly tools, and DeepL as superior in accuracy and naturalness. Despite generally positive attitudes toward MT, users highlight critical areas for improvement, such as interface usability and limited language support. Moreover, the reviews reflect broader socio-cultural dynamics, illustrating how societal narratives shape MT adoption. This study underscores the complementary role of MT alongside human translation and offers practical insights for developers, practitioners, educators, and researchers. By exploring user-driven insights, this research advances understanding of the evolving landscape of MT and its integration into professional and educational contexts.