Analyzing the impact of unemployment on mental health among Chinese university graduates: a study of emotional and linguistic patterns on Weibo.

Purpose: This study explores the intricate relationship between unemployment rates and emotional responses among Chinese university graduates, analyzing how these factors correlate with specific linguistic features on the popular social media platform Sina Weibo. The goal is to uncover patterns that elucidate the psychological and emotional dimensions of unemployment challenges among this demographic. Methods: The analysis utilized a dataset of 30,540 Sina Weibo posts containing specific keywords related to unemployment and anxiety, collected from January 2019 to June 2023. The posts were pre-processed to eliminate noise and refine the data quality. Linear regression and textual analyses were employed to identify correlations between unemployment rates for individuals aged 16-24 and the linguistic characteristics of the posts. Results: The study found significant fluctuations in urban youth unemployment rates, peaking at 21.3% in June 2023. A corresponding increase in anxiety-related expressions was noted in the social media posts, with peak expressions aligning with high unemployment rates. Linguistic analysis revealed that the category of "Affect" showed a strong positive correlation with unemployment rates, indicating increased emotional expression alongside rising unemployment. Other categories such as "Negative emotion" and "Sadness" also showed significant correlations, highlighting a robust relationship between economic challenges and emotional distress. Conclusion: The findings underscore the profound impact of unemployment on the emotional well-being of university students, suggesting that economic hardships are closely linked to psychological stress and heightened negative emotions. This study contributes to a holistic understanding of the socio-economic challenges faced by young adults, advocating for comprehensive support systems that address both the economic and psychological facets of unemployment.

Linguistic-based Mild Cognitive Impairment detection using Informative Loss.

This paper presents a deep learning method using Natural Language Processing (NLP) techniques, to distinguish between Mild Cognitive Impairment (MCI) and Normal Cognitive (NC) conditions in older adults. We propose a framework that analyzes transcripts generated from video interviews collected within the I-CONECT study project, a randomized controlled trial aimed at improving cognitive functions through video chats. Our proposed NLP framework consists of two Transformer-based modules, namely Sentence Embedding (SE) and Sentence Cross Attention (SCA). First, the SE module captures contextual relationships between words within each sentence. Subsequently, the SCA module extracts temporal features from a sequence of sentences. This feature is then used by a Multi-Layer Perceptron (MLP) for the classification of subjects into MCI or NC. To build a robust model, we propose a novel loss function, called InfoLoss, that considers the reduction in entropy by observing each sequence of sentences to ultimately enhance the classification accuracy. The results of our comprehensive model evaluation using the I-CONECT dataset show that our framework can distinguish between MCI and NC with an average area under the curve of 84.75%.

The Association Between Linguistic Characteristics of Physicians' Communication and Their Economic Returns: Mixed Method Study.

BACKGROUND: Web-based health care has the potential to improve health care access and convenience for patients with limited mobility, but its success depends on active physician participation. The economic returns of internet-based health care initiatives are an important factor that can motivate physicians to continue their participation. Although several studies have examined the communication patterns and influences of web-based health consultations, the correlation between physicians' communication characteristics and their economic returns remains unexplored. OBJECTIVE: This study aims to investigate how the linguistic features of 2 modes of physician-patient communication, instrumental and affective, determine the physician's economic returns, measured by the honorarium their patients agree to pay per consultation. We also examined the moderating effects of communication media (web-based text messages and voice messages) and the compounding effects of different communication features on economic returns. METHODS: We collected 40,563 web-based consultations from 528 physicians across 4 disease specialties on a large, web-based health care platform in China. Communication features were extracted using linguistic inquiry and word count, and we used multivariable linear regression and K-means clustering to analyze the data. RESULTS: We found that the use of cognitive processing language (ie, words related to insight, causation, tentativeness, and certainty) in instrumental communication and positive emotion-related words in affective communication were positively associated with the economic returns of physicians. However, the extensive use of discrepancy-related words could generate adverse effects. We also found that the use of voice messages for service delivery magnified the effects of cognitive processing language but did not moderate the effects of affective processing language. The highest economic returns were associated with consultations in which the physicians used few expressions related to negative emotion; used more terms associated with positive emotions; and later, used instrumental communication language. CONCLUSIONS: Our study provides empirical evidence about the relationship between physicians' communication characteristics and their economic returns. It contributes to a better understanding of patient-physician interactions from a professional-client perspective and has practical implications for physicians and web-based health care platform executives.

An approach for assisting diagnosis of Alzheimer's disease based on natural language processing.

Introduction: Alzheimer's Disease (AD) is a common dementia which affects linguistic function, memory, cognitive and visual spatial ability of the patients. Language is proved to have the relationship with AD, so the time that AD can be diagnosed in a doctor's office is coming. Methods: In this study, the Pitt datasets are used to detect AD which is balanced in gender and age. First bidirectional Encoder Representation from Transformers (Bert) pretrained model is used to acquire the word vector. Then two channels are constructed in the feature extraction layer, which is, convolutional neural networks (CNN) and long and short time memory (LSTM) model to extract local features and global features respectively. The local features and global features are concatenated to generate feature vectors containing rich semantics, which are sent to softmax classifier for classification. Results: Finally, we obtain a best accuracy of 89.3% which is comparative compared to other studies. In the meanwhile, we do the comparative experiments with TextCNN and LSTM model respectively, the combined model manifests best and TextCNN takes the second place. Discussion: The performance illustrates the feasibility to predict AD effectively by using acoustic and linguistic datasets.

Examining the Supports and Advice That Women With Intimate Partner Violence Experience Received in Online Health Communities: Text Mining Approach.

BACKGROUND: Intimate partner violence (IPV) is an underreported public health crisis primarily affecting women associated with severe health conditions and can lead to a high rate of homicide. Owing to the COVID-19 pandemic, more women with IPV experiences visited online health communities (OHCs) to seek help because of anonymity. However, little is known regarding whether their help requests were answered and whether the information provided was delivered in an appropriate manner. To understand the help-seeking information sought and given in OHCs, extraction of postings and linguistic features could be helpful to develop automated models to improve future help-seeking experiences. OBJECTIVE: The objective of this study was to examine the types and patterns (ie, communication styles) of the advice offered by OHC members and whether the information received from women matched their expressed needs in their initial postings. METHODS: We examined data from Reddit using data from subreddit community r/domesticviolence posts from November 14, 2020, through November 14, 2021, during the COVID-19 pandemic. We included posts from women aged ≥18 years who self-identified or described experiencing IPV and requested advice or help in this subreddit community. Posts from nonabused women and women aged <18 years, non-English posts, good news announcements, gratitude posts without any advice seeking, and posts related to advertisements were excluded. We developed a codebook and annotated the postings in an iterative manner. Initial posts were also quantified using Linguistic Inquiry and Word Count to categorize linguistic and posting features. Postings were then classified into 2 categories (ie, matched needs and unmatched needs) according to the types of help sought and received in OHCs to capture the help-seeking result. Nonparametric statistical analysis (ie, 2-tailed t test or Mann-Whitney U test) was used to compare the linguistic and posting features between matched and unmatched needs. RESULTS: Overall, 250 postings were included, and 200 (80%) posting response comments matched with the type of help requested in initial postings, with legal advice and IPV knowledge achieving the highest matching rate. Overall, 17 linguistic or posting features were found to be significantly different between the 2 groups (ie, matched help and unmatched help). Positive title sentiment and linguistic features in postings containing health and wellness wordings were associated with unmatched needs postings, whereas the other 14 features were associated with postings with matched needs. CONCLUSIONS: OHCs can extract the linguistic and posting features to understand the help-seeking result among women with IPV experiences. Features identified in this corpus reflected the differences found between the 2 groups. This is the first study that leveraged Linguistic Inquiry and Word Count to shed light on generating predictive features from unstructured text in OHCs, which could guide future algorithm development to detect help-seeking results within OHCs effectively.

Explainable Alzheimer's Disease Detection Using Linguistic Features from Automatic Speech Recognition.

INTRODUCTION: Alzheimer's disease (AD) is the most prevalent type of dementia and can cause abnormal cognitive function and progressive loss of essential life skills. Early screening is thus necessary for the prevention and intervention of AD. Speech dysfunction is an early onset symptom of AD patients. Recent studies have demonstrated the promise of automated acoustic assessment using acoustic or linguistic features extracted from speech. However, most previous studies have relied on manual transcription of text to extract linguistic features, which weakens the efficiency of automated assessment. The present study thus investigates the effectiveness of automatic speech recognition (ASR) in building an end-to-end automated speech analysis model for AD detection. METHODS: We implemented three publicly available ASR engines and compared the classification performance using the ADReSS-IS2020 dataset. Besides, the SHapley Additive exPlanations algorithm was then used to identify critical features that contributed most to model performance. RESULTS: Three automatic transcription tools obtained mean word error rate texts of 32%, 43%, and 40%, respectively. These automated texts achieved similar or even better results than manual texts in model performance for detecting dementia, achieving classification accuracies of 89.58%, 83.33%, and 81.25%, respectively. CONCLUSION: Our best model, using ensemble learning, is comparable to the state-of-the-art manual transcription-based methods, suggesting the possibility of an end-to-end medical assistance system for AD detection with ASR engines. Moreover, the critical linguistic features might provide insight into further studies on the mechanism of AD.

Detecting Narcissism From Older Adults' Daily Language Use: A Machine Learning Approach.

OBJECTIVES: Narcissism has been associated with poorer quality social connections in late life, yet less is known about how narcissism is associated with older adults' daily social interactions. This study explored the associations between narcissism and older adults' language use throughout the day. METHODS: Participants aged 65-89 (N = 281) wore electronically activated recorders which captured ambient sound for 30 s every 7 min across 5-6 days. Participants also completed the Narcissism Personality Inventory-16 scale. We used Linguistic Inquiry and Word Count to extract 81 linguistic features from sound snippets and applied a supervised machine learning algorithm (random forest) to evaluate the strength of links between narcissism and each linguistic feature. RESULTS: The random forest model showed that the top 5 linguistic categories that displayed the strongest associations with narcissism were first-person plural pronouns (e.g., we), words related to achievement (e.g., win, success), to work (e.g., hiring, office), to sex (e.g., erotic, condom), and that signal desired state (e.g., want, need). DISCUSSION: Narcissism may be demonstrated in everyday life via word use in conversation. More narcissistic individuals may have poorer quality social connections because their communication conveys an emphasis on self and achievement rather than affiliation or topics of interest to the other party.

Performance of machine learning algorithms for dementia assessment: impacts of language tasks, recording media, and modalities.

OBJECTIVES: Automatic speech and language assessment methods (SLAMs) can help clinicians assess speech and language impairments associated with dementia in older adults. The basis of any automatic SLAMs is a machine learning (ML) classifier that is trained on participants' speech and language. However, language tasks, recording media, and modalities impact the performance of ML classifiers. Thus, this research has focused on evaluating the effects of the above-mentioned factors on the performance of ML classifiers that can be used for dementia assessment. METHODOLOGY: Our methodology includes the following steps: (1) Collecting speech and language datasets from patients and healthy controls; (2) Using feature engineering methods which include feature extraction methods to extract linguistic and acoustic features and feature selection methods to select most informative features; (3) Training different ML classifiers; and (4) Evaluating the performance of ML classifiers to investigate the impacts of language tasks, recording media, and modalities on dementia assessment. RESULTS: Our results show that (1) the ML classifiers trained with the picture description language task perform better than the classifiers trained with the story recall language task; (2) the data obtained from phone-based recordings improves the performance of ML classifiers compared to data obtained from web-based recordings; and (3) the ML classifiers trained with acoustic features perform better than the classifiers trained with linguistic features. CONCLUSION: This research demonstrates that we can improve the performance of automatic SLAMs as dementia assessment methods if we: (1) Use the picture description task to obtain participants' speech; (2) Collect participants' voices via phone-based recordings; and (3) Train ML classifiers using only acoustic features. Our proposed methodology will help future researchers to investigate the impacts of different factors on the performance of ML classifiers for assessing dementia.

Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts.

BACKGROUND AND OBJECTIVE: With the rapid development of information dissemination technology, the amount of events information contained in massive texts now far exceeds the intuitive cognition of humans, and it is hard to understand the progress of events in order of time. Temporal information runs through the whole process of beginning, proceeding, and ending of events, and plays an important role in many natural language processing applications, such as information extraction, question answering, and text summary. Accurately extracting temporal information from Chinese texts and automatically mapping the temporal expressions in natural language to the time axis are crucial to understanding the development of events and dynamic changes in them. METHODS: This study proposes a method integrating machine learning with linguistic features (IMLLF) for extraction and normalization of temporal expressions in Chinese texts to achieve the above objectives. Linguistic features are constructed by analyzing the expression rules of temporal information, and are combined with machine learning to map the natural language form of time onto a one-dimensional timeline. The web text dataset we build is divided into five parts for five-fold cross-validation, to compare the influence of different combinations of linguistic features and different methods. In the open medical dialog dataset, based on the training model obtained from the web text dataset, 200 disease descriptions are randomly selected each time for three rounds of experiments. RESULTS: The F1 of multi-feature fusion is 95.2%, which is better than the single-feature and double-feature combination. The results of experiments showed that the proposed IMLLF method can improve the accuracy of recognition of temporal information in Chinese to a greater extent than classical methods, with an F1-score of over 95% on the web text dataset and medical conversation dataset. In terms of the normalization of time expressions, the accuracy of the IMLLF method is higher than 93%. CONCLUSIONS: IMLLF has better results in extracting and normalizing time expressions on the web text dataset and the medical conversation dataset, which verifies the universality of IMLLF to identify and quantify temporal information. IMLLF method can accurately map the time information to the time axis, which is convenient for doctors to intuitively see when and what happened to the patient, and helps to make better medical decisions.

Predicting Generalized Anxiety Disorder From Impromptu Speech Transcripts Using Context-Aware Transformer-Based Neural Networks: Model Evaluation Study.

BACKGROUND: The ability to automatically detect anxiety disorders from speech could be useful as a screening tool for an anxiety disorder. Prior studies have shown that individual words in textual transcripts of speech have an association with anxiety severity. Transformer-based neural networks are models that have been recently shown to have powerful predictive capabilities based on the context of more than one input word. Transformers detect linguistic patterns and can be separately trained to make specific predictions based on these patterns. OBJECTIVE: This study aimed to determine whether a transformer-based language model can be used to screen for generalized anxiety disorder from impromptu speech transcripts. METHODS: A total of 2000 participants provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test (TSST). They also completed the Generalized Anxiety Disorder 7-item (GAD-7) scale. A transformer-based neural network model (pretrained on large textual corpora) was fine-tuned on the speech transcripts and the GAD-7 to predict whether a participant was above or below a screening threshold of the GAD-7. We reported the area under the receiver operating characteristic curve (AUROC) on the test data and compared the results with a baseline logistic regression model using the Linguistic Inquiry and Word Count (LIWC) features as input. Using the integrated gradient method to determine specific words that strongly affect the predictions, we inferred specific linguistic patterns that influence the predictions. RESULTS: The baseline LIWC-based logistic regression model had an AUROC value of 0.58. The fine-tuned transformer model achieved an AUROC value of 0.64. Specific words that were often implicated in the predictions were also dependent on the context. For example, the first-person singular pronoun "I" influenced toward an anxious prediction 88% of the time and a nonanxious prediction 12% of the time, depending on the context. Silent pauses in speech, also often implicated in predictions, influenced toward an anxious prediction 20% of the time and a nonanxious prediction 80% of the time. CONCLUSIONS: There is evidence that a transformer-based neural network model has increased predictive power compared with the single word-based LIWC model. We also showed that the use of specific words in a specific context-a linguistic pattern-is part of the reason for the better prediction. This suggests that such transformer-based models could play a useful role in anxiety screening systems.

Data-driven analytics of COVID-19 'infodemic'.

The rampant of COVID-19 infodemic has almost been simultaneous with the outbreak of the pandemic. Many concerted efforts are made to mitigate its negative effect to information credibility and data legitimacy. Existing work mainly focuses on fact-checking algorithms or multi-class labeling models that are less aware of the intrinsic characteristics of the language. Nor is it discussed how such representations can account for the common psycho-socio-behavior of the information consumers. This work takes a data-driven analytical approach to (1) describe the prominent lexical and grammatical features of COVID-19 misinformation; (2) interpret the underlying (psycho-)linguistic triggers in terms of sentiment, power and activity based on the affective control theory; (3) study the feature indexing for anti-infodemic modeling. The results show distinct language generalization patterns of misinformation of favoring evaluative terms and multimedia devices in delivering a negative sentiment. Such appeals are effective to arouse people's sympathy toward the vulnerable community and foment their spreading behavior.

Comparing product quality between translation and paraphrasing: Using NLP-assisted evaluation frameworks.

Translation and paraphrasing, as typical forms in second language (L2) communication, have been considered effective learning methods in second language acquisition (SLA). While many studies have investigated their similarities and differences in a process-oriented approach, little attention has been paid to the correlation in product quality between them, probably due to difficulties in assessing the quality of translation and paraphrasing. Current quality evaluation methods tend to be either subjective and one-sided or lack consistency and standardization. To address these limitations, we proposed preliminary evaluation frameworks for translation and paraphrasing by incorporating indices from natural language processing (NLP) tools into teachers' rating rubrics and further compared the product quality of the two activities. Twenty-nine translators were recruited to perform a translation task (translating from Chinese to English) and a paraphrasing task (paraphrasing in English). Their output products were recorded by key-logging technique and graded by three professional translation teachers by using a 10-point Likert Scale. This rating process adopted rubrics consisting of both holistic and analytical assessments. Besides, indices containing textual features from lexical and syntactic levels were extracted from TAASSC and TAALES. We identified indices that effectively predicted product quality using Pearson's correlation analysis and combined them with expert evaluation rubrics to establish NLP-assisted evaluation frameworks for translation and paraphrasing. With the help of these frameworks, we found a closely related performance between the two tasks, evidenced by several shared predictive indices in lexical sophistication and strong positive correlations between translated and paraphrased text quality according to all the rating metrics. These similarities suggest a shared language competence and mental strategies in different types of translation activities and perhaps in other forms of language tasks. Meanwhile, we also observed differences in the most salient textual features between translations and paraphrases, mainly due to the different processing costs required by the two tasks. These findings enrich our understanding of the shared ground and divergences in product quality between translation and paraphrasing and shed light on the pedagogical application of translation activities in classroom teaching. Moreover, the proposed evaluation framework can also bring insights into the development of standardized evaluation frameworks in translation and paraphrasing in the future.

A corpus-based investigation of the English translations of Mao Zedong's speeches.

This study adopts a corpus-based approach to examine the linguistic features manifested in the English translations of Mao Zedong's speeches, taking Winston Churchill's speeches (representative of normalized spoken texts) and the spoken texts in BNC Sampler (representative of original spoken texts) as the reference corpora. By investigating the macro- and micro-linguistic features, it is found that the translated Mao's speeches (both direct and inverse translations) differ from normalized spoken texts as well as original spoken texts in three aspects: (i) macro-linguistic features, (ii) the use of personal pronouns, (iii) the use of modal verbs. In terms of macro-linguistic features, the average word length of the English translations is higher than that of normalized spoken texts and that of original spoken texts; the standardized type/token ratio and average sentence length of the English translations are higher than those of original spoken texts, but lower than those of normalized spoken texts. Meanwhile, in terms of the use of personal pronouns, the English translations of Mao's speeches prefer the underuse of the first person singular pronoun I. Furthermore, as far as modal verbs are concerned, the English translations of Mao's speeches prefer the overuse of must and should on the one hand, and the underuse of shall, could, may, and would on the other hand. Therefore, it can be said that the translated Mao's speeches exhibit some particular linguistic features, which can not only be differentiated from normalized spoken texts, but also be distinguished from original spoken texts. They are in a middle position in relation to normalized spoken texts as well as original spoken texts. This in-betweenness not only exhibits Mao's creative and idiosyncratic language use, but also reflects the influence of the language transfer from Chinese into English.

Analysis on the linguistic features of conflict discourse in mathematical cooperation problem solving in China.

Classroom teaching is a kind of social activity system. Thus, as a form of classroom learning, collaborative problem solving has a strong social attribute. It is extremely common to choose the conflict discourse in the context of cooperation. The verbal characteristics of the conflicting discourse level in cooperative mathematics problem solving directly affects the cooperative learning between students and the classroom teaching of teachers. This article focuses on the overall linguistic characteristics of conflict discourse in solving cooperative problems and the discourse style and language characteristics of the three stages of conflict discourse. The main research conclusions are as follows: (1) The classification of language features of conflict discourse includes extreme summaries, negation, discourse markers, and so on. Among them, the frequency of Indexical 2nd-person pronouns is the highest. (2) The language expressions at the "initial stage of conflict" include Explanatory statement Negative response, instruct refuse and Seditious inquiry Confrontational answer. The language shows the characteristics of using emphatic words or phrases, negative words, imperative sentences and so on. Meanwhile, rebuttal questions, direct responses, explanations, and negative avoidance are the main forms language expressions at the "conflict stage." It also exhibits the verbal characteristics of rhetorical questions, negative comments, and direct negation. Lastly, topic-shifting, compromise, third-party intervention, and one-sided wins are the linguistic expressions at the "end of conflict." The language features are the appearance of tone relaxation and language easing, and the conflict ending utterances reflect cooperation.

Screening for Generalized Anxiety Disorder From Acoustic and Linguistic Features of Impromptu Speech: Prediction Model Evaluation Study.

BACKGROUND: Frequent interaction with mental health professionals is required to screen, diagnose, and track mental health disorders. However, high costs and insufficient access can make frequent interactions difficult. The ability to assess a mental health disorder passively and at frequent intervals could be a useful complement to the conventional treatment. It may be possible to passively assess clinical symptoms with high frequency by characterizing speech alterations collected using personal smartphones or other wearable devices. The association between speech features and mental health disorders can be leveraged as an objective screening tool. OBJECTIVE: This study aimed to evaluate the performance of a model that predicts the presence of generalized anxiety disorder (GAD) from acoustic and linguistic features of impromptu speech on a larger and more generalizable scale than prior studies did. METHODS: A total of 2000 participants were recruited, and they participated in a single web-based session. They completed the Generalized Anxiety Disorder-7 item scale assessment and provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test. We used the linguistic and acoustic features that were found to be associated with anxiety disorders in previous studies along with demographic information to predict whether participants fell above or below the screening threshold for GAD based on the Generalized Anxiety Disorder-7 item scale threshold of 10. Separate models for each sex were also evaluated. We reported the mean area under the receiver operating characteristic (AUROC) from a repeated 5-fold cross-validation to evaluate the performance of the models. RESULTS: A logistic regression model using only acoustic and linguistic speech features achieved a significantly greater prediction accuracy than a random model did (mean AUROC 0.57, SD 0.03; P<.001). When separately assessing samples from female participants, we observed a mean AUROC of 0.55 (SD 0.05; P=.01). The model constructed from the samples from male participants achieved a mean AUROC of 0.57 (SD 0.07; P=.002). The mean AUROC increased to 0.62 (SD 0.03; P<.001) on the all-sample data set when demographic information (age, sex, and income) was included, indicating the importance of demographics when screening for anxiety disorders. The performance also increased for the female sample to a mean of 0.62 (SD 0.04; P<.001) when using demographic information (age and income). An increase in performance was not observed when demographic information was added to the model constructed from the male samples. CONCLUSIONS: A logistic regression model using acoustic and linguistic speech features, which have been suggested to be associated with anxiety disorders in prior studies, can achieve above-random accuracy for predicting GAD. Importantly, the addition of basic demographic variables further improves model performance, suggesting a role for speech and demographic information to be used as automated, objective screeners of GAD.

Neural tracking as a diagnostic tool to assess the auditory pathway.

When a person listens to sound, the brain time-locks to specific aspects of the sound. This is called neural tracking and it can be investigated by analysing neural responses (e.g., measured by electroencephalography) to continuous natural speech. Measures of neural tracking allow for an objective investigation of a range of auditory and linguistic processes in the brain during natural speech perception. This approach is more ecologically valid than traditional auditory evoked responses and has great potential for research and clinical applications. This article reviews the neural tracking framework and highlights three prominent examples of neural tracking analyses: neural tracking of the fundamental frequency of the voice (f0), the speech envelope and linguistic features. Each of these analyses provides a unique point of view into the human brain's hierarchical stages of speech processing. F0-tracking assesses the encoding of fine temporal information in the early stages of the auditory pathway, i.e., from the auditory periphery up to early processing in the primary auditory cortex. Envelope tracking reflects bottom-up and top-down speech-related processes in the auditory cortex and is likely necessary but not sufficient for speech intelligibility. Linguistic feature tracking (e.g. word or phoneme surprisal) relates to neural processes more directly related to speech intelligibility. Together these analyses form a multi-faceted objective assessment of an individual's auditory and linguistic processing.

Linguistic features and psychological states: A machine-learning based approach.

Previous research mostly used simplistic measures and limited linguistic features (e.g., personal pronouns, absolutist words, and sentiment words) in a text to identify its author's psychological states. In this study, we proposed using additional linguistic features, that is, sentiments polarities and emotions, to classify texts of various psychological states. A large dataset of forum posts including texts of anxiety, depression, suicide ideation, and normal states were experimented with machine-learning algorithms. The results showed that the proposed linguistic features with machine-learning algorithms, namely Support Vector Machine and Deep Learning achieved a high level of performance in the detection of psychological state. The study represents one of the first attempts that uses sentiment polarities and emotions to detect texts of psychological states, and the findings may contribute to our understanding of how accuracy may be enhanced in the detection of various psychological states. Significance and suggestions of the study are also offered.

Improving neural machine translation with POS-tag features for low-resource language pairs.

Integrating linguistic features has been widely utilized in statistical machine translation (SMT) systems, resulting in improved translation quality. However, for low-resource languages such as Thai and Myanmar, the integration of linguistic features in neural machine translation (NMT) systems has yet to be implemented. In this study, we propose transformer-based NMT models (transformer, multi-source transformer, and shared-multi-source transformer models) using linguistic features for two-way translation of Thai-to-Myanmar, Myanmar-to-English, and Thai-to-English. Linguistic features such as part-of-speech (POS) tags or universal part-of-speech (UPOS) tags are added to each word on either the source or target side, or both the source and target sides, and the proposed models are conducted. The multi-source transformer and shared-multi-source transformer models take two inputs (i.e., string data and string data with POS tags) and produce string data or string data with POS tags. A transformer model that utilizes only word vectors was used as the first baseline model for comparison with the proposed models. The second baseline model, an Edit-Based Transformer with Repositioning (EDITOR) model, was also used to compare with our proposed models in addition to the baseline transformer model. The findings of the experiments show that adding linguistic features to the transformer-based models enhances the performance of a neural machine translation in low-resource language pairs. Moreover, the best translation results were yielded using shared-multi-source transformer models with linguistic features resulting in more significant Bilingual Evaluation Understudy (BLEU) scores and character n-gram F-score (chrF) scores than the baseline transformer and EDITOR models.

Acoustic and Linguistic Features of Impromptu Speech and Their Association With Anxiety: Validation Study.

BACKGROUND: The measurement and monitoring of generalized anxiety disorder requires frequent interaction with psychiatrists or psychologists. Access to mental health professionals is often difficult because of high costs or insufficient availability. The ability to assess generalized anxiety disorder passively and at frequent intervals could be a useful complement to conventional treatment and help with relapse monitoring. Prior work suggests that higher anxiety levels are associated with features of human speech. As such, monitoring speech using personal smartphones or other wearable devices may be a means to achieve passive anxiety monitoring. OBJECTIVE: This study aims to validate the association of previously suggested acoustic and linguistic features of speech with anxiety severity. METHODS: A large number of participants (n=2000) were recruited and participated in a single web-based study session. Participants completed the Generalized Anxiety Disorder 7-item scale assessment and provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test. Acoustic and linguistic speech features were a priori selected based on the existing speech and anxiety literature, along with related features. Associations between speech features and anxiety levels were assessed using age and personal income as covariates. RESULTS: Word count and speaking duration were negatively correlated with anxiety scores (r=-0.12; P<.001), indicating that participants with higher anxiety scores spoke less. Several acoustic features were also significantly (P<.05) associated with anxiety, including the mel-frequency cepstral coefficients, linear prediction cepstral coefficients, shimmer, fundamental frequency, and first formant. In contrast to previous literature, second and third formant, jitter, and zero crossing rate for the z score of the power spectral density acoustic features were not significantly associated with anxiety. Linguistic features, including negative-emotion words, were also associated with anxiety (r=0.10; P<.001). In addition, some linguistic relationships were sex dependent. For example, the count of words related to power was positively associated with anxiety in women (r=0.07; P=.03), whereas it was negatively associated with anxiety in men (r=-0.09; P=.01). CONCLUSIONS: Both acoustic and linguistic speech measures are associated with anxiety scores. The amount of speech, acoustic quality of speech, and gender-specific linguistic characteristics of speech may be useful as part of a system to screen for anxiety, detect relapse, or monitor treatment.

Linguistic features of suicidal thoughts and behaviors: A systematic review.

Language is a potential source of predictors for suicidal thoughts and behaviors (STBs), as changes in speech characteristics, communication habits, and word choice may be indicative of increased suicide risk. We reviewed the current literature on STBs that investigated linguistic features of spoken and written language. Specifically, we performed a search in linguistic, medical, engineering, and general databases for studies that investigated linguistic features as potential predictors of STBs published in peer-reviewed journals until the end of November 2021.We included 75 studies that investigated 279,032 individuals with STBs (age = 29.53 ± 10.29, 35% females). Of those, 34 (45%) focused on lexicon, 20 (27%) on prosody, 15 (20%) on lexicon and first-person singular, four (5%) on (morpho)syntax, and two (3%) were unspecified. Suicidal thoughts were predicted by more intensifiers and superlatives, while suicidal behaviors were predicted by greater usage of pronouns, changes in the amount of verb usage, more prepend and multifunctional words, more nouns and prepositions, and fewer modifiers and numerals. A diverse field of research currently investigates linguistic predictors of STBs, and more focus is needed on their specificity for either suicidal thoughts or behaviors.