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Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice. This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

In this paper, we apply an information-theoretic method proposed by Ryabko and Savina (therefore called the RS-method), based on the use of data compression, to recognize the individual author’s style of a writer across four languages from different language groups and families. In this paper, the presented method was used to study fiction texts in Russian (East Slavic group of languages of the Indo-European language family), Amharic (South Ethiosemitic group of the Semitic language family), Chinese (Sinitic group of the Sino-Tibetan language family) and English (West Germanic language group of the Indo-European language family). It was found that the amount of data necessary for recognizing an author’s style is almost the same for all four languages, i.e., the amount of data is invariant across different language groups. The results obtained are of interest to computer science, literary studies, linguistics and, in particular, computational linguistics.

The integration of AI with social media platforms is significantly influencing English language skills, reshaping how users engage with and learn the language. So far, no studies have specifically examined how AI affects English reading for first-year non-English major undergraduates in Saudi Arabia. This study investigates how Meta-AI combined with WhatsApp enhances reading comprehension. Two intact classes were randomly chosen and assigned to experimental (N=43) and control (N=49) groups in a quasi-experimental study. The experimental group received instruction through AI-enhanced social media, namely WhatsApp, while the control group continued with a conventional approach. Data were collected before and after treatment. Statistical analysis revealed that the experimental group enhanced reading comprehension significantly more than the control group, which received conventional teaching. The study implies that integrating AI with social media platforms like WhatsApp can improve substantially English reading comprehension, suggesting a beneficial shift towards technology-driven learning methods in education.

The increasing advance of mobile devices and wireless technologies has generated great interest in ubiquitous learning (u-learning) among academia, practitioners, and policy makers. However, design elements that incorporate learning styles and learning strategies into u-learning system applications in English as a Foreign Language (EFL) education are still limited. There are two objectives in this research. First, we developed a Ubiquitous Learning Instruction System with Augment Reality features (UL-IAR) to improve the performance of EFL learning with authentic situations. Second, we examined whether different learning strategies and cognitive styles affect learning performance in using UL-IAR. We conducted field experiments to investigate the appropriation of learning strategies and cognitive styles in using UL-IAR to learn EFL. The results showed that learning strategies and users' cognitive styles affect learning performance in using UL-IAR. Individuals with field dependent cognitive style fit enforcing learning strategy better than other users who are field independent and mix field cognitive styles. Our findings provide theoretical and practical insights for pedagogies that are suitable for u-learning. Our findings also contribute to the practice of AR and u-learning system development.

AR/VR and other immersive technologies are creating dynamic, learner-centred, and engaging language-learning environments. In these ever-changing situations, judging someone’s language abilities is difficult. Managing multimodal learner inputs, understanding model predictions, and protecting user data across distributed systems are some of the most prominent challenges. This paper proposes TriNet-AQ, a federated, interpretable deep learning architecture for classifying English competency in AR/VR platforms. This technique addresses the difficulties raised. This work employs Quantum Sinusoidal Encoding (QSE), Triaxial Attention Fusion (TAF) for multimodal feature alignment, and Quantum Modulated Integration (QMI) to enhance context-aware learning by optimizing temporal representation. Hybrid Slime Gorilla Optimisation (HSGO) aids optimization. It accelerates convergence and improves performance and economy. TriNet-AQ provides decentralized training to many clients via federated learning, enhancing privacy and flexibility. TriNet-AQ outperforms classical, fuzzy, and hybrid baselines in real-world augmented and virtual reality instructional datasets. Its accuracy is 98.5%, AUC is 0.95, and EPES is 0.89. Even when it loses 3.5% accuracy on new data, it can generalize effectively. Another SHAP-based interpretability finding is the presence of obvious feature attributions and consistent relevance across users. Statistical analysis, including Cohen’s d = 0.89 (p < 0.001), confirms the model’s significance and reliability. TriNet-AQ provides robust, easy-to-understand, and private real-time, tailored language evaluation in next-generation immersive learning environments.

Tonal harmony is one of the central organization systems of Western music. This article characterizes the statistical foundations of tonal harmony based on the computational analysis of expert annotations in a large corpus. Using resampling methods, this study shows that 1) the rank-frequency distribution of chords resembles a power law, i.e. few chords govern a large proportion of the data; 2) chord transitions are referential and chord predictability is significantly affected by distinguished chord features; 3) tonal harmony conveys directedness in time; and 4) tonal harmony operates differently at the hierarchical levels of chords and keys. These results serve to characterize tonal harmony on empirical grounds and advance the methodological state-of-the-art in digital musicology.

This paper explores the semantic-symbolic correspondences of vowel sounds in the Kazakh language. The aim of the study is to identify manifestations of sound symbolism in Kazakh through experimental and statistical methods. Associations between the vowel sounds A, Ä, İ, O, and Ū and ten semantic oppositions (large–small, white–black, light–dark, male–female, strong–weak) were assessed. A total of 67 Kazakh-speaking participants took part in a survey conducted via Google Forms using the semantic differential method. The data were processed in Jamovi software, calculating mean, standard deviation, and median values. The results indicate that the correspondence between sounds and meanings is non-random. For instance, the sounds “O” and “Ū” are associated with concepts such as “large”, “strong”, and “male”, while “İ” and “Ä” are linked to “small”, “weak”, and “female”. The sound “A” was rated highly for opposing meanings, revealing its universal nature. These findings demonstrate that sound symbolism in Kazakh is grounded in phonetic, cognitive, and cultural dimensions. The results offer deeper insight into the phonosemantic system of the Kazakh language and provide new empirical data illustrating the natural connection between language and thought.

Additional language speakers (ALSs) often experience anxiety due to challenges posed by their nonstandard pronunciation. Building on these insights, this paper introduces an instrument, the Accent Anxiety Scale (AAS), specifically designed to assess three sources of anxiety that are experienced by ALSs, including (a) apprehension about negative evaluations from other individuals due to their distinctive speech style, (b) concerns about rejection from the target language community because of their “foreign” pronunciation, and (c) anxieties over potential communication hurdles attributed to the intelligibility of their pronunciation. We evaluated the psychometric robustness of the AAS by analyzing data from a total of 474 immigrant and international student ALSs at a predominantly English-speaking Canadian university. Study 1 focused on immigrants (N = 203) and employed exploratory factor and correlational analyses to isolate a concise number of internally consistent and valid items for each subscale. Study 2 extended these analyses to international students (N = 153) and employed confirmatory factor and correlation analyses to further validate the AAS in this population. Study 3 examined international students (N = 118) at two time points to establish the AAS’s temporal stability. These studies yielded robust psychometric evidence for the factor structure, reliability, and validity of the AAS. The findings not only support the use of the AAS as a research instrument but also offer implications for pedagogical strategies aimed at alleviating ALSs’ accent anxiety.