Enablers or Inhibitors? Unpacking the Emotional Power Behind In-Vehicle AI Anthropomorphic Interaction: A Dual-Factor Approach by Text Mining
通过文本分析技术识别新能源汽车用户对车载AI拟人化交互的爱与厌恶情感，并验证多模态交互对提升用户满意度的作用，为缓解新技术抵抗提供参考。
The intelligent strategy of the new energy vehicle (NEV) industry has triggered the rapid prevalence of in-vehicle anthropomorphic artificial intelligence (AI) assistants. There is still a lack of clarity regarding NEV users' attitudes toward this cutting-edge technology and whether they receive a satisfactory intelligent service experience. To circumvent potential emerging technology resistance, in this article, we utilize text analysis techniques for the identification of AI interaction emotions, love and disgust (enablers and inhibitors) with significant influence on user satisfaction, and validates the improving role of multimodality on the effectiveness of anthropomorphic interaction. In addition, this study innovatively constructs a multidimensional corpus of modality × emotion, using structural topic modeling to uncover the constituent elements and real-time changes of love and disgust emotions in different modalities, from which development opportunities and improvement directions for AI anthropomorphic interaction technologies are identified. The findings provide new insights into the application of emotion analysis methods to improve users' intelligent service experience and provide a realistic reference for mitigating emerging technology resistance in the NEV industry.