인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Objective: This study aims to analyze and categorize user experience of smartphones by utilizing social media data (Twitter). Background: Observing user experience is critical in identifying users’ implicit needs. Previous studies on observation techniques have mainly been conducted in forced and artificial environments. So far, it was difficult to observe user’s behavior in natural context. Social media (e.g. Facebook, MySpace, Twitter, etc.) can be helpful for observing variant and natural UX with users’ words. It is a potentially valuable source of data that can be used to delve into the thoughts of millions of people. Method: In this study, to gather user experience of smart product, techniques that mining external data (e.g., twitter and blog) were used. From the mined external data, user experiences were categorized according to the product smartness and identify the relationship between the product smartness and positive/negative experiences. Results: A total of 19,288 tweets including ‘smartphone’ were collected from 2014.06.01 ~ 2014.08.31. Among them, a total of 699 tweets are actually related to user experiences of smartphones. The collected tweets were categorized according to the dimension of product smartness and the reason of user’s emotion. According to the results, there were many positive experiences for all of dimensions, but there were negative experiences only for multi-functionality and connectivity. Conclusion: The study suggested that a mining technique can be used to gather and analyze user experience effectively and quantitatively without bias. Application: It is expected that the proposed method could be helpful for understanding user’s implicit needs on the products.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2016-530-002088608
