Details

Autor(en) / Beteiligte

• Wang, Cai
• Kim, Soo Yeon
• Song, Yein
• Kim, Sungho
• Choi, Minsik
• Seu, Donghoon
• Yun, Myung Hwan

Titel

Text Mining for Exploring UX Issues of Qualitative Think Aloud Data on EV Sound

Ist Teil von

• 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2022, p.0200-0203

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• The study aims to explore the UX issues of electric vehicle (EV) sound by mining qualitive think aloud text data. Electric vehicle, which is more eco-friendly than traditional vehicle, becomes a popular development trend in the automotive industry. The user's preference for sound can have a great influence on EV purchases. Think aloud is widely-used method to collect users' thoughts and needs. 40 participants join in the experiment by speaking their own opinions on EV sound while driving. Then, text mining is conducted to explore UX issues from qualitative think aloud data. The UX labels are generated according to word frequency, and ten UX labels are divided into five UX aspects. Sentiment analysis is also taken and the associated words with UX labels are generated. Finally, insights based on the five UX aspects (speed, mode, vehicle component, environment, and sound type) on EV sound have been drawn. The outcomes can suggest implications for EV sound designing.