Recent psychological research shown that the places where we live are linked to our personality traits. Geographical aggregation of personalities has been observed in many individualistic nations; notably, the mountainousness is an essential component in understanding regional variances in personality. Could mountainousness therefore also explain the clustering of personality-types in collectivist countries like China? Using a nationwide survey (29,838 participants) in Mainland China, we investigated the relationship between the Big Five personality traits and mountainousness indicators at the provincial level. Multilevel modelling showed significant negative associations between the elevation coefficient of variation (Elevation CV) and the Big Five personality traits, whereas mean elevation (Elevation Mean) and the standard deviation in elevation (Elevation STD) were positively associated with human personalities. Subsequent machine learning analyses showed that, for example, Elevation Mean outperformed other mountainousness indicators regarding correlations with neuroticism, while Elevation CV performed best relative to openness models. Our results mirror some previous findings, such as the positive association between openness and Elevation STD, while also revealing cultural differences, such as the social desirability of people living in China’s mountainous areas.

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Melody and lyrics, reflecting two unique human cognitive abilities, are usually combined in music to convey emotions. Although psychologists and computer scientists have made considerable progress in revealing the association between musical structure and the perceived emotions of music, the features of lyrics are relatively less discussed. Using linguistic inquiry and word count (LIWC) technology to extract lyric features in 2,372 Chinese songs, this study investigated the effects of LIWC-based lyric features on the perceived arousal and valence of music. First, correlation analysis shows that, for example, the perceived arousal of music was positively correlated with the total number of lyric words and the mean number of words per sentence and was negatively correlated with the proportion of words related to the past and insight. The perceived valence of music was negatively correlated with the proportion of negative emotion words. Second, we used audio and lyric features as inputs to construct music emotion recognition (MER) models. The performance of random forest regressions reveals that, for the recognition models of perceived valence, adding lyric features can significantly improve the prediction effect of the model using audio features only; for the recognition models of perceived arousal, lyric features are almost useless. Finally, by calculating the feature importance to interpret the MER models, we observed that the audio features played a decisive role in the recognition models of both perceived arousal and perceived valence. Unlike the uselessness of the lyric features in the arousal recognition model, several lyric features, such as the usage frequency of words related to sadness, positive emotions, and tentativeness, played important roles in the valence recognition model.

Recently, smart products have not only demonstrated more functionality and technical capabilities but have also shown a trend towards emotional expression. Emotional design plays a crucial role in smart products as it not only influences users’ perception and evaluation of the product but also promotes collaborative communication between users and the product. In the future, emotional design of smart products needs to be regarded as an important comprehensive design issue, rather than simply targeting a specific element. It should consider factors such as design systems, values, business strategies, technical capabilities, design ethics, and cultural responsibilities. However, currently, there is a lack of a design model that combines these elements. Currently, there are numerous practices in emotional design for smart products from different perspectives. They provide us an opportunity to build a comprehensive design model based on a large number of design case studies. Therefore, this study employed a standardized grounded theory approach to investigate 80 smart products and conducted interviews with 12 designers to progressively code and generate a design model. Through the coding process, this research extracted 547 nodes and gradually formed 10 categories, ultimately resulting in a design model comprising 5 sequential steps. This model includes user requirements, concept definition, design ideation, design implementation, and evaluation, making it applicable to most current and future emotional design issues in smart products.