Associations between lyric and musical depth in Chinese songs: Evidence from computational modeling.

Musical depth, which encompasses the intellectual and emotional complexity of music, is a robust dimension that influences music preference. However, there remains a dearth of research exploring the relationship between lyrics and musical depth. This study addressed this gap by analyzing linguistic inquiry and word count-based lyric features extracted from a comprehensive dataset of 2372 Chinese songs. Correlation analysis and machine learning techniques revealed compelling connections between musical depth and various lyric features, such as the usage frequency of emotion words, time words, and insight words. To further investigate these relationships, prediction models for musical depth were constructed using a combination of audio and lyric features as inputs. The results demonstrated that the random forest regressions (RFR) that integrated both audio and lyric features yielded superior prediction performance compared to those relying solely on lyric inputs. Notably, when assessing the feature importance to interpret the RFR models, it became evident that audio features played a decisive role in predicting musical depth. This finding highlights the paramount significance of melody over lyrics in effectively conveying the intricacies of musical depth.

The relationship between depression severity and heart rate variability in children and adolescents: A meta-analysis.

OBJECTIVE: Depression in children and adolescents has gradually attracted social attention. Heart rate variability (HRV) has been found to be influenced by depression severity, but results have not been uniformed in children and adolescents. This study investigated the relationship between depression severity and heart rate variability in children and adolescents, aiming to provide additional evidence for an objective, effective, and convenient depression screening tool in this population. METHODS: Literature searching was conducted in China National Knowledge Infrastructure (CNKI), Wanfang Data, Web of Science, PubMed, ScienceDirect, and EBSCO. Relevant studies investigating the relationship between depression severity and HRV in children and adolescents were selected for meta-analysis. RESULTS: 31 articles were included in this meta-analysis, involving 4534 participants. Depression severity in children and adolescents was significantly negatively correlated with high frequency (HF) and root mean square of successive differences (RMSSD) in HRV (HF: r = -0.10, 95% CI: -0.17 to -0.04, p = 0.001; RMSSD: r = -0.18, 95% CI: -0.30 to -0.05, p = 0.01). The relationship between HF and depression severity was moderated by age, higher among those aged >12 than among those aged <12 (r = -0.17, -0.02, Q = 7.32, p = 0.007). CONCLUSION: Heart rate variability is associated with depression severity in children and adolescents.

Mental time travel ability influences the representation of events and emotional expressions: evidence from microblogs.

Mental time travel (MTT) ability allows people to project themselves mentally into the past and future. It is associated with people's mental representation of events and objects. Using text analysis methods, we explore the linguistic representation and emotional expression of people with various MTT abilities. In Study 1, we assessed the users' MTT distances, text lengths, visual perspectives, priming effects of temporal words, and emotional valences by analyzing 2973 users' microblog texts. From our statistical analysis findings, users with far MTT incorporated longer text length and more third-person pronouns in their microblogs and are more likely to relate the future and past with the present than people with near MTT. However, the study showed no significant difference in emotional valence between people with different MTT distances. In Study 2, we explored the relationship between emotional valence and MTT ability by analyzing the comments of 1112 users on "procrastination." We found the users with far MTT more positive toward procrastination than those with near MTT. By analyzing users' social media platform data, this study re-examined and verified previous findings indicating that users who mentally travel different temporal distances represent events and emotional expressions differently. This study serves as an important reference for MTT studies.

Human Personality Is Associated with Geographical Environment in Mainland China.

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.

What a deep song: The role of music features in perceived depth.

This study examines perceptions of music depth by exploring its relationships to different music features. First, a correlation analysis shows that the perceived depth of music is negatively correlated with valence and arousal and is also related to different music features, including tempo, Mel-frequency cepstrum coefficients, chromagrams, spectral centroids, spectral bandwidth, spectral contrast, spectral flatness, spectral roll-off, and tonal centroid features. Applying machine learning methods, we find that selected music features can predict perceptions of music depth, and a random forest regression (RFR) is found to perform best in this study. Finally, a feature importance analysis shows that the principal component of spectral contrast dominates the RFR-based music depth recognition model, showing that deep music usually has clear and narrow-band audio signals.

Using machine learning analysis to interpret the relationship between music emotion and lyric features.

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.

Chinese App User's Needs Profile: From Questionnaire Measurement to Behavior Analysis.

With the widespread use of mobile devices, the Apps people install and use could be closely linked to their needs. A precise profile of the needs of the user has become a vital foundation of the experience of the user. Previous studies mainly rely on self-reporting to understand the subjective attitudes of the App user toward a single App. This research combined questionnaire measurement and behavior analysis to profile the needs of the App user from a broader perspective. Based on the theoretical model of previous research studies, study 1 developed a novel needs questionnaire measurement of a Chinese App user, which showed good reliability and validity. In study 2, authorized App usage data were collected to construct the behavioral needs profile of a Chinese user. The results showed that the primary needs of the Chinese user remained a relatively high consistency between the questionnaire and the behavior data. The questionnaire-based and behavioral data-based needs profiles provide a reference for further personalized user experience design.

Cross-situation consistency of mobile App users' psychological needs.

Previous studies showed that individuals' traits could be used to explain the similarity of behavioral patterns across different occasions. Such studies have typically focused on personality traits, and have not been extended to psychological needs. Our study used a large dataset of 1,715,078 anonymous users' App usage records to examine whether the individual's needs-based profiles of App usage were consistent across different situations (as indexed by categories of App functions). Results showed a high level of consistency across situations in a user's choice of Apps based on the needs the Apps could satisfy. These results provide clear evidence in support of cross-category App recommendation systems.