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.

The aim of this study was to explore the causes of tourists’ lesser enjoyment of theme parks through an unsupervised machine learning approach—structural topic modelling. Specifically, the research extracted a comprehensive list of discussion topics about the travel experience of tourists through the analysis of 112,000 customer reviews released by visitors to the Shanghai Disney Resort from 16 June 2016 to 4 March 2022. Then, we used sentiment analysis to distinguish positive and negative topics and to explore the relationship between tourists who buy different ticket types and sentiments in negative topics. The results show that problems such as “parking,” “service attitude,” “recommendation feeling,” “experience comparison,” and “entrance” may be the main reasons for an unpleasant experience. In addition, we also found that when tourists travel in groups (e.g., via family tickets), customers feel unhappy because of parking and fast track problems. Moreover, when tourists travel alone or with small groups, staff service attitudes, experience comparisons, and entrance processes are the sources of greater concern. Our findings can help theme park managers to better understand the expectations of tourists and take effective measures to tackle issues causing customer dissatisfaction, and they can also contribute to theme park studies in tourism management.

This study investigates how language arousal in Generative AI systems influences users' interaction willingness, examining the roles of social identity and visual atmosphere. Drawing on the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP) and Social Identity Theory, we constructed a theoretical model integrating language arousal, social identity, visual atmosphere, and interaction willingness, and analyzed 8,809 interactions from Character.AI using multimodal methods combining linguistic analysis and visual processing. Our findings reveal that high-arousal language significantly increases interaction willingness, with social identity mediating this relationship. Most notably, we discovered a “psychological defense-curiosity paradox”: shadow visual atmospheres, despite triggering initial defensive reactions, enhance engagement more effectively than light atmospheres, challenging conventional “brighter is better” design assumptions. This research advances theory by repositioning language arousal as a direct causal variable in AI interaction, extending cognitive processing models to human-AI contexts, and demonstrating how visual elements strategically modulate psychological responses. These insights provide valuable direction for developing emotionally intelligent AI systems that effectively balance linguistic stimulation and visual atmosphere to create more engaging human-AI experiences.

During the normalization stage of the COVID-19 epidemic prevention and control, the safety threats caused by improper epidemic prevention measures of airlines have become the primary concern for air passengers. Negative e-WOM related to safety perception obtained based on online multimodal reviews of travel websites has become an important decision-making basis for potential air passengers when making airline choices. This study aims to examine the relationship between potential air passengers’ negative safety perception and the usefulness of online reviews, as well as to test the moderating effect of review modality and airline type. It also further explores the effectiveness and feasibility of applying big data sentiment analysis to e-WOM management. To this end, the theoretical model of negative safety perception, review modality, and airline type affecting review usefulness was constructed. Then we select 10 low-cost airlines and 10 full-service airlines, respectively, according to the number of reviews sorted by the TripAdvisor website, and use crawling techniques to obtain 10,485 reviews related to COVID-19 safety of the above companies from December 2019 to date, and conduct safety perception sentiment analysis based on Python’s Textblob library. Finally, to avoid data overdispersion, the model is empirically analyzed by negative binomial regression using R software. The results indicate that (1) Negative safety perception significantly and negatively affects review usefulness, that is, extreme negative safety perception can provide higher review usefulness for potential air passengers. (2) Review modality and airline type have a significant moderating effect on the relationship between negative safety perception and review usefulness, in which multimodal reviews and full-service airlines both weakened the negative impact of negative safety perception on review usefulness. The theoretical model in this paper is both an extension of the application of big data sentiment analysis techniques and a beneficial supplement to current research findings of e-WOM, providing an important reference for potential air passengers to identify useful reviews accurately and thus reduce safety risks in online decision-making.