Natural Language Analysis Research Articles

A deep learning model for syncope recognition in the emergency department

Abstract Introduction Syncope is a common presenting symptom in the emergency departments (EDs) and poses challenges in its recognition and differentiation from alternative causes of transient loss of consciousness. Natural Language Processing (NLP) is an artificial intelligence technique that enables the analysis of natural language, such as clinical notes and medical history within electronic medical records (EMRs). The application of NLP into the medical field is surging but its use for the recognition of syncope is still underexplored. Purpose We aimed to develop a NLP-based deep learning model for syncope recognition in the ED. Methods Two models, based on the Italian and Multilingual Bidirectional Encoder Representations from Transformers (BERT) language model, were developed and evaluated using consecutive EMRs from Humanitas Research Hospital ED. The first "triage" model was only based on textual data contained in the "triage" section of the EMR. The second "anamnesis" model also considered textual data contained in the "medical history" section. Performance metrics including area under the curve (AUC), Matthew’s correlation coefficient (MCC) and Brier’s score (BS) were used to compare the discriminative capabilities of different BERT models. Results The models were developed and tested on 15,098 and 15,222 EMRs, respectively. The Multilanguage BERT discriminated patients with syncope with an AUC of 0.85 in the triage model and of 0.92 in the anamnesis model. The Italian BERT identified patients with syncope with a MCC of 0.50 and a BS of 0.02 in the triage model and of 0.64 and 0.01, respectively, in the anamnesis model. Conclusions We propose two NLP-based models that identify syncope patients with a high discriminative capability. The presented algorithm may provide a starting point for the management of syncope patients using machine learning techniques.ROC and calibration curves of modelsFramework for model implementation

Exploring adult hearing aid user experiences: meaning extraction methods, content patterns, and associations with demographic and outcome variables

Objective Employing automated language analysis, specifically Meaning Extraction Method (MEM) and Principal Component Analysis (PCA), to identify key factors in open-text responses about hearing aid experiences. Design Exploratory, cross-sectional design, using an online questionnaire. Responses to a single open-ended question were analysed using MEM, PCA, regression, and correlation analyses. Study sample Participants (n = 538) included adult hearing aid users sampled from the Hearing Tracker website community and Lexie Hearing user databases in the United States. Results The MEM-derived items revealed six factors related to hearing aid experiences: (1) life change, (2) social situation, (3) quality of life, (4) impact and speech understanding, (5) communication and interaction, and (6) music and environmental sounds. IOI-HA item 3 had the most statistically significant correlations with PCA factors. Quantile regression revealed that factors one and two significantly predicted the IOI-HA total score. Positive correlations were observed between self-reported hearing difficulty and factors one, four, and five, as well as between factor one and general health and factor two and physical activity. Conclusion Natural language analysis of open-ended textual responses can offer valuable insights into hearing aid users’ experiences. Future studies should aim to refine this methodology to enhance clinical relevance and generalisability.

Unraveling the Experience of Affection Across Marital and Friendship Interactions

AbstractFeelings of affection (i.e., fondness and intense positive regard) are a hallmark of close relationships. Existing studies have often examined affection as a trait or behavior, but rarely as an emotional experience in social interactions. In two dyadic observational interaction studies (total N = 314 individuals), 49 US-based married couples (age range: 21–65) and 108 friendship dyads (age range: 15–26) engaged in two naturalistic 10-min conversations about (a) a topic of disagreement (conflict conversation) and (b) something they enjoyed doing together (pleasant conversation) and reported on their subjective emotional experiences (e.g., affection, using an emotion checklist following each conversation) and relationship satisfaction. Conversation transcripts were analyzed using natural language analysis. Results showed that experiences of affection were most strongly associated with experiences of compassion, amusement, and excitement, as well as with less anger, and were unrelated to most other negative emotions. Natural language analysis further showed that greater positive (but not negative) emotional tone was associated with greater affection (especially among friends in the pleasant conversations). Levels of affection were consistently higher in pleasant versus conflict conversations and similar across marital versus friendship interactions. Finally, experiences of affection were associated with greater relationship satisfaction for friends across conversations and for spouses (women) in the pleasant conversation. These findings contribute to our understanding of affection as a foundational emotional experience in close relationships and highlight avenues for future research.

Leveraging large language models through natural language processing to provide interpretable machine learning predictions of mental deterioration in real time

AbstractBased on official estimates, 50 million people worldwide are affected by dementia, and this number increases by 10 million new patients every year. Without a cure, clinical prognostication and early intervention represent the most effective ways to delay its progression. To this end, artificial intelligence and computational linguistics can be exploited for natural language analysis, personalized assessment, monitoring, and treatment. However, traditional approaches need more semantic knowledge management and explicability capabilities. Moreover, using large language models (llms) for cognitive decline diagnosis is still scarce, even though these models represent the most advanced way for clinical–patient communication using intelligent systems. Consequently, we leverage an llm using the latest natural language processing (nlp) techniques in a chatbot solution to provide interpretable machine learning prediction of cognitive decline in real-time. Linguistic-conceptual features are exploited for appropriate natural language analysis. Through explainability, we aim to fight potential biases of the models and improve their potential to help clinical workers in their diagnosis decisions. More in detail, the proposed pipeline is composed of (i) data extraction employing nlp-based prompt engineering; (ii) stream-based data processing including feature engineering, analysis, and selection; (iii) real-time classification; and (iv) the explainability dashboard to provide visual and natural language descriptions of the prediction outcome. Classification results exceed 80% in all evaluation metrics, with a recall value for the mental deterioration class about 85%. To sum up, we contribute with an affordable, flexible, non-invasive, personalized diagnostic system to this work.

People believe political opponents accept blatant moral wrongs, fueling partisan divides.

Efforts to bridge political divides often focus on navigating complex and divisive issues, but eight studies reveal that we should also focus on a more basic misperception: that political opponents are willing to accept basic moral wrongs. In the United States, Democrats, and Republicans overestimate the number of political outgroup members who approve of blatant immorality (e.g. child pornography, embezzlement). This "basic morality bias" is tied to political dehumanization and is revealed by multiple methods, including natural language analyses from a large social media corpus and a survey with a representative sample of Americans. Importantly, the basic morality bias can be corrected with a brief, scalable intervention. Providing information that just one political opponent condemns blatant wrongs increases willingness to work with political opponents and substantially decreases political dehumanization.

Emotional tone in clinical high risk for psychosis: novel insights from a natural language analysis approach.

Individuals at clinical high risk (CHR) for psychosis experience subtle emotional disturbances that are traditionally difficult to assess, but natural language processing (NLP) methods may provide novel insight into these symptoms. We predicted that CHR individuals would express more negative emotionality and less emotional language when compared to controls. We also examined associations with symptomatology. Participants included 49 CHR individuals and 42 healthy controls who completed a semi-structured narrative interview. Interview transcripts were analyzed using Linguistic Inquiry and Word Count (LIWC) to assess the emotional tone of the language (tone -the ratio of negative to positive language) and count positive/negative words used. Participants also completed clinical symptom assessments to determine CHR status and characterize symptoms (i.e., positive and negative symptom domains). The CHR group had more negative emotional tone compared to healthy controls (t=2.676, p=.009), which related to more severe positive symptoms (r2=.323, p=.013). The percentages of positive and negative words did not differ between groups (p's>.05). Language analyses provided accessible, ecologically valid insight into affective dysfunction and psychosis risk symptoms. Natural language processing analyses unmasked differences in language for CHR that captured language tendencies that were more nuanced than the words that are chosen.

Research on Chinese Financial Industry Application Scenarios based on ChatGPT

The emergence of the artificial intelligence ChatGPT has brought a huge information impact to the traditional industry, promoting the deep integration of ChatGPT and financial institutions, stimulating its innovation vitality, and comprehensively empowering financial services. This technology is the key to promote the digital intelligence transformation of the financial industry. Based on the systematic study of the integration scenario of ChatGPT and innovative application in the financial industry and related limitations, this paper constructively puts forward suggestions and expectations for the future development of the financial industry under the background of artificial intelligence, to make some innovative exploration for the application of ChatGPT in the financial industry. According to the research, ChatGPT-like artificial intelligence's powerful natural language processing, data sorting, and analysis capabilities have great application value for financial institutions covering customer service, operation, marketing, risk control, investment and budget, etc., but there are also problems such as high cost, low credibility and security, and technical limitations. Finally, this paper puts forward effective empowerment suggestions based on three aspects: government policy, institutions and technology development.

Neurocomputer System of Semantic Analysis of the Text in the Kazakh Language

The purpose of the study is to solve an extreme mathematical problem—semantic analysis of natural language, which can be used in various fields, including marketing research, online translators, and search engines. When training the neural network, data training methods based on the latent Dirichlet allocation model and vector representation of words were used. This study presents the development of a neurocomputer system used for the purpose of semantic analysis of the text in the Kazakh language, based on machine learning and the use of the latent Dirichlet allocation model. In the course of the study, the stages of system development were considered, regarding the text recognition algorithm. The Python programming language was used as a tool using libraries that greatly simplify the process of creating neural networks, including the Keras library. An experiment was conducted with the involvement of experts to test the effectiveness of the system, the results of which confirmed the reliability of the data provided by the system. The papers of modern computer linguists dealing with the problems of natural language processing using various technologies and methods are considered.

Natural Language Analysis for the Identification of Mental Disturbances in Social Networks: A Systematic Review of Studies

Las enfermedades mentales constituyen una de las principales causas de angustia en la vida de las personas a nivel individual, y repercuten en la salud y el bienestar de la sociedad. Para captar estas complejas asociaciones, las ciencias computacionales y la comunicación, a través del uso de métodos de procesamiento del lenguaje natural (NLP) en datos recolectados en redes sociales, han aportado prometedores avances para potenciar la atención sanitaria mental proactiva y ayudar al diagnóstico precoz. Por ello, se realizó una revisión sistemática de la literatura acerca de la detección de alteraciones mentales a través de redes sociales, mediante el uso de NLP en los últimos 5 años, que permitió identificar métodos, tendencias y orientaciones futuras, a través del análisis de 73 estudios, de 509 que arrojó la revisión de documentos extraídos de bases de datos científicas. El estudio reveló que, los fenómenos más comúnmente estudiados, correspondieron a Depresión e Ideación suicida, identificados a través del uso de algoritmos como el LIWC, CNN, LSTM, RF y SVM, en datos extraídos principalmente de Reddit y Twitter. Este estudio, finalmente proporciona algunas recomendaciones sobre las metodologías de NLP para la detección de enfermedades mentales, que pueden ser adoptadas en el ejercicio de profesionales interesados en la salud mental, y algunas reflexiones sobre el uso de estas tecnologías.

Mental Health Prediction Model on Social Media Data Using CNN-BiLSTM

Social media has transformed into a global platform for expression and interaction where users can share photos, images, and videos. The rapid development and widespread use of social media afford the opportunity to analyze the construction of social life in societies and communities. As a result of alterations in lifestyle during the COVID-19 pandemic, mental health disorders increased. Mental health is a complex disease involving numerous individual, socioeconomic, and clinical variables. Natural language processing and analysis methods are required to address this complexity. The classification of mental health-related texts, which can serve as early warnings and early diagnoses, is facilitated by analytical and natural language processing techniques. In this investigation, a CNN-BiLSTM model was utilized, which was aided by a FastText-based word weighting method. The utilized data set consists of texts on mental health with labels such as borderline personality disorder (BPD), anxiety, depression, bipolar, mentalillness, schizophrenia, and poison. There are 35000 training records and 6108 test records. The data will undergo a data cleansing procedure, which will include lower text stages, number removal, reading mark removal, and stopword removal. Modeling with CNN-BiLSTM and FastText weighting yielded an F1-Score and accuracy of 85% and 85%, respectively. In comparison to the Bi-LSTM model, the F1-Score and accuracy were both 83%.

Comparing the language style of heads of state in the US, UK, Germany and Switzerland during COVID-19

The COVID-19 pandemic posed a global threat to nearly every society around the world. Individuals turned to their political leaders to safely guide them through this crisis. The most direct way political leaders communicated with their citizens was through official speeches and press conferences. In this report, we compare psychological language markers of four different heads of state during the early stage of the pandemic. Specifically, we collected all pandemic-related speeches and press conferences delivered by political leaders in the USA (Trump), UK (Johnson), Germany (Merkel), and Switzerland (Swiss Federal Council) between February 27th and August 31st, 2020. We used natural language analysis to examine language markers of expressed positive and negative emotions, references to the community (we-talk), analytical thinking, and authenticity and compare these language markers across the four nations. Level differences in the language markers between the leaders can be detected: Trump’s language was characterized by a high expression of positive emotion, Merkel’s by a strong communal focus, and Johnson’s and the Swiss Federal Council by a high level of analytical thinking. Overall, these findings mirror different strategies used by political leaders to deal with the COVID-19 pandemic.

ICD-10 WHO Search With RAKE Algorithm

In most cases, clinicians do not use the ICD-10 standard established by the WHO for diagnosing diseases. These issues may result in outcomes that are undesirable from the standpoints of patient safety and the law. The WHO ICD-10 standard collection of diagnoses that can’t be effectively searched using MySQL’s native search mechanism. Therefore, in order to automatically produce several keywords for each ICD-10 code, academics are interested in analyzing the natural language analysis of WHO ICD-10 data. When diagnosing illnesses, it is envisaged that the availability of numerous types of keywords can lead to more fruitful search results. Natural language analysis, a technique for removing stop words from sentences and simultaneously assessing the semantics of the language from which the keywords will be extracted, makes it possible to do this. Keywords: Natural Language Analysis ; ICD-10 ; Stop Words ; Semantics.

DB-BERT: making database tuning tools “read” the manual

DB-BERT is a database tuning tool that exploits information gained via natural language analysis of manuals and other relevant text documents. It uses text to identify database system parameters to tune as well as recommended parameter values. DB-BERT applies large, pre-trained language models (specifically, the BERT model) for text analysis. During an initial training phase, it fine-tunes model weights in order to translate natural language hints into recommended settings. At run time, DB-BERT learns to aggregate, adapt, and prioritize hints to achieve optimal performance for a specific database system and benchmark. Both phases are iterative and use reinforcement learning to guide the selection of tuning settings to evaluate (penalizing settings that the database system rejects while rewarding settings that improve performance). In our experiments, we leverage hundreds of text documents about database tuning as input for DB-BERT. We compare DB-BERT against various baselines, considering different benchmarks (TPC-C and TPC-H), metrics (throughput and run time), as well as database systems (PostgreSQL and MySQL). The experiments demonstrate clearly that DB-BERT benefits from combining general information about database tuning, mined from text documents, with scenario-specific insights, gained via trial runs. The full source code of DB-BERT is available online at https://itrummer.github.io/dbbert/.

Laughing at death: Facebook, the ‘haha’ reaction, and death coverage on local US news pages

ABSTRACT Is it inappropriate to “like” a Facebook post announcing death? That was the concern of Meta’s leadership in 2016, when they implemented emoji reactions (e.g. sadness and love) to help users more appropriately respond to tragic events. The expectation was that these new reactions would promote compassion in the face of grief, but in the years since many observers now label the “haha” laughing emoji as a particularly menacing reaction used to troll the misfortune of others. To date though, little academic work has sought to explore such claims. Across hundreds of millions of reactions to death on the platform spanning nearly a decade, this paper demonstrates the size and scope of the haha trolling phenomenon on Facebook—including its exponential growth during the pandemic. Using the automated tools of natural language analysis, this study also sheds some light on the potential motivations behind this socially deviant behavior.

Triangulation of Questionnaires, Qualitative Data and Natural Language Processing: A Differential Approach to Religious Bahá'í Fasting in Germany.

Approaches to integrating mixed methods into medical research are gaining popularity. To get a holistic understanding of the effects of behavioural interventions, we investigated religious fasting using a triangulation of quantitative, qualitative, and natural language analysis. We analysed an observational study of Bahá'í fasting in Germany using a between-method triangulation that is based on links between qualitative and quantitative analyses. Individual interviews show an increase in the mindfulness and well-being categories. Sentiment scores, extracted from the interviews through natural language processing, positively correlate with questionnaire outcomes on quality of life (WHO-5: Spearman correlation r = 0.486, p = 0.048). Five questionnaires contribute to the first principal component capturing the spectrum of mood states (50.1% explained variance). Integrating the findings of the between-method triangulation enabled us to converge on the underlying effects of this kind of intermittent fasting. TRIAL REGISTRATION: NCT03443739.

Types and Type Theories in Natural Language Analysis

This article reviews the set of possible paths from a semantics based on Simple Type Theories (STTs) toward one based on Rich Type Theories (RTTs) and the motivations behind the move from one to the other. The main elements of this review are threefold. First, it provides a systematic overview of different STTs, including options for what to include as members of the set of basic types, and whether to assume type constructors additional to the one for constructing functional types. Second, this review discusses the main differences between STTs and RTTs, namely, that in RTTs but not in STTs, types are part of the object language. That is, one can refer to and reason with and about types. In turn, this makes available an alternative account of propositions to the one assumed in semantics in the Frege–Church–Montague tradition: Instead of being characterized as sets of possible worlds, propositions can be treated themselves as types, that is, as structured semantic objects. Third and finally, this review provides an outline of some of the main applications of RTTs, including hyperintensionality, quantification, anaphora, polysemy, and modification.

Improving the Performance of Autonomous Driving through Deep Reinforcement Learning

Reinforcement learning (RL) is revolutionizing the artificial intelligence (AI) domain and significantly aiding in building autonomous systems with a higher level comprehension of the world as we observe it. Deep learning (DL) facilitates RL to scale and resolve previously intractable problems, for instance, allowing supervision principles designed for robots to be acquired directly from visual data, developing video game proficiency from pixel-level information, etc. Recent research shows that RL algorithms help represent problems dealing with high-dimensional, unprocessed data input and can have successful applications in computer vision, pattern identification, natural language analysis, and speech parsing. This research paper focuses on training a simulation model of a car to navigate autonomously on a racetrack using RL. The study explores several fundamental algorithms in Deep RL, namely Proximal Policy Optimization (PPO), Deep Q-network (DQN), and Deep Deterministic Policy Gradient (DDPG). The paper documents a comparative analysis of these three prominent algorithms—based on their speed, accuracy, and overall performance. After a thorough evaluation, the research indicates that the DQN surpassed the other existing algorithms. This study further examined the performance of the DQN with and without ε-decay and observed that the DQN with ε-decay is better suited for our objective and is significantly more stable than its non ε-decay counterpart. The findings from this research could assist in improving the performance and stability of autonomous vehicles using the DQN with ε -decay. It concludes by discussing the fine-tuning of the model for future real-world applications and the potential research areas within the field of autonomous driving.

An Integrated Data-Driven Procedure for Product Specification Recommendation Optimization with LDA-LightGBM and QFD

E-commerce and social media have become increasingly essential and influential for sustainable business growth, particularly due to the COVID-19 pandemic, which has permanently altered the business landscape. The vast amount of consumer data available online holds significant potential and value. The strategic utilization of this information can expedite the research and development of new products, leading to shorter product cycles and increased innovation. This study explores the effectiveness of employing the latent Dirichlet allocation (LDA) method and various deep learning technologies to predict Amazon consumer ratings. We propose a product service system that utilizes natural language analyses of online sales data and user reviews, enabling industries to quickly identify and respond to market demands. We present a data-driven procedure for the customer-to-manufacturer (C2M) business model, specifically focusing on sustainable data-driven business models based on knowledge and innovation management. This procedure analyzes user comments on online shopping platforms to match product requirements and features, optimize product values, and address issues related to product specifications and new product development planning. The results of the business verification demonstrate that this procedure accurately evaluates product specifications under different demands, facilitates effective product planning, and enhances research and development decision making. This approach, based on sustainable data-driven business models and knowledge and innovation management, expands market opportunities for the sector and improves overall production efficiency, starting from the research and development stage.

Can Large Language Models Predict Data Correlations from Column Names?

Recent publications suggest using natural language analysis on database schema elements to guide tuning and profiling efforts. The underlying hypothesis is that state-of-the-art language processing methods, so-called language models, are able to extract information on data properties from schema text. This paper examines that hypothesis in the context of data correlation analysis: is it possible to find column pairs with correlated data by analyzing their names via language models? First, the paper introduces a novel benchmark for data correlation analysis, created by analyzing thousands of Kaggle data sets (and available for download). Second, it uses that data to study the ability of language models to predict correlation, based on column names. The analysis covers different language models, various correlation metrics, and a multitude of accuracy metrics. It pinpoints factors that contribute to successful predictions, such as the length of column names as well as the ratio of words. Finally, the study analyzes the impact of column types on prediction performance. The results show that schema text can be a useful source of information and inform future research efforts, targeted at NLP-enhanced database tuning and data profiling.

Uncovering the Social-Cognitive Contributors to Social Dysfunction in Borderline Personality Disorder Through Language Analysis.

Borderline personality disorder (BPD) is characterized by severe interpersonal dysfunction, yet the underlying nature of such dysfunction remains poorly understood. The present study adopted a behavioral approach to more objectively describe the social-cognitive contributors to interpersonal dysfunction in BPD. Participants (N = 530) completed an online survey comprising validated measures of BPD features and other problematic interpersonal traits (e.g., narcissism), as well as a writing prompt where they were asked to share their personal thoughts about relationships. Computerized language analysis methods were used to quantify various psychosocial dimensions of participants' writing, which were incorporated into a principal component analysis. Analyses revealed four core social dimensions of thought: (1) Connectedness/Intimacy; (2) Immediacy; (3) Social Rumination; (4) Negative Affect. All four dimensions correlated with BPD features in intuitive ways, some of which were specific to BPD. This study highlights the value of natural language analysis to explore fundamental dimensions of personality disorder.