Natural Language Processing Methods Research Articles

Intelligent Identification of Cryptographic Ciphers using Machine Learning Techniques

This research work demonstrates cipher-type identification methods using machine learning algorithms. Cipher-type identification is a recent research interest to do better cryptanalysis of an encryption algorithm in a minimal time. Along with the increased security issues, obfuscation is being used with encryption algorithms to keep them hidden. This is when the ciphertext identification challenge came into play. The ciphertext classification challenge was performed using both image processing and natural language processing methods. For image processing purposes, CNN was utilized; whereas text-CNN, transformers and BERT models were used as natural language processing tools. In order to train the proposed machine learning based classification models, two types of datasets were generated: image data and text data. This study compares the experimental outcomes derived from various architectural CNN, Transformer, and BERT models. We also present a comparative study of our research work with another research works which are done in the recent past. The proposed BERT model is found to be the most efficient model for the correct classification of ciphertext over other transformer and CNN-based classification models. This work will surely help the cryptanalyst to perform cryptanalysis of an encryption algorithm in a minimal time.

Second language learning of degree expressions: A computational approach

Abstract Degrees, unlike entities or events, refer to comparative qualities and are closely tied to gradable adjectives such as “tall.” Degree expressions have been explored in second language (L2) research, covering areas such as learnability, first language (L1) transfer, contrastive analysis, and acquisition difficulty. However, a computational approach to the learning of degree expressions in L2 contexts, particularly for L1 Chinese learners of English, has not been thoroughly investigated. This study aims to fill this gap by utilizing natural language processing (NLP) methods, drawing insights from recent advancements in large language models (LLMs). This study extends Cong (2024)’s general-purpose assessment pipeline to specifically analyze degree expressions, predicting that surprisal metrics will correlate with proficiency levels and distinct developmental stages of L2 learners. Crucially, we address the limitations of surprisal metrics in capturing underuse or avoidance—common in L2 development—by integrating frequency-based analyses. Using an NLP pipeline developed with Stanza, we automatically identified and analyzed degree expressions, constructing linear mixed-effects models to track L2 development trajectories. Our findings reveal that as proficiency increases, learners use complex degree expressions more frequently, supporting theories linking difficulty and learnability. Higher surprisal values are associated with lower proficiency in using degree expressions, and these surprisals are more predictive of degree expressions proficiency than classic NLP measures. These results add further evidence that LLMs and NLP tools provide valuable insights into L2 development, specifically in the domain of degree expressions, expanding upon previous research and offering new approaches for understanding L2 learning processes.

A Framework for Applying Machine Learning and Natural Language Processing Methods to Accounting Recognition

SYNOPSIS Accounting recognition involves determining how certain transactions or events should be included in accounting records, which serve as the foundation for financial statements. However, the recognition process can be complex and error prone. In this study, we propose a framework that combines machine learning and natural language processing methods to develop a model capable of understanding the nature of a transaction and automating intelligent recognition. We demonstrate the feasibility of our framework by constructing preferred stock recognition models. Using 102 prospectuses of preferred stocks issued in China from 2004 to 2022, the models demonstrate their ability to identify preferred shares as either financial liabilities or equity instruments based on an understanding of transaction nature. We contribute to the accounting literature and practice by revealing the effectiveness of artificial intelligence in facilitating intelligent accounting recognition. Data Availability: The data used in this study are publicly available from the websites of the China Securities Regulatory Commission, the Shanghai Stock Exchange, and the Shenzhen Stock Exchange. JEL Classifications: M41; C45.

Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotation

Background:Suicide is a leading cause of death worldwide, making early identification of suicidal behaviors crucial for clinicians. Current Natural Language Processing (NLP) approaches for identifying suicidal behaviors in Electronic Health Records (EHRs) rely on keyword searches, rule-based methods, and binary classification, which may not fully capture the complexity and spectrum of suicidal behaviors. This study aims to create a multi-class labeled dataset with annotation guidelines and develop a novel NLP approach for fine-grained, multi-label classification of suicidal behaviors, improving the efficiency of the annotation process and accuracy of the NLP methods. Methods:We develop a multi-class labeling system based on guidelines from FDA, CDC, and WHO, distinguishing between six categories of suicidal behaviors and allowing for multiple labels per data sample. To efficiently create an annotated dataset, we use an MPNet-based semantic retrieval framework to extract relevant sentences from a large EHR dataset, reducing annotation space while capturing diverse expressions. Experts annotate the extracted sentences using the multi-class system. We then formulate the task as a multi-label classification problem and fine-tune transformer-based models on the curated dataset to accurately classify suicidal behaviors in EHRs. Results:Lexical analysis revealed key themes in assessing suicide risk, considering an individual’s history, mental health, substance use, and family background. Fine-tuned transformer-based models effectively identified suicidal behaviors from EHRs, with Bio_ClinicalBERT, BioBERT, and XLNet achieving the F1 scores (0.81), outperforming BERT and RoBERTa. The proposed approach, based on a multi-label classification system, captures the complexity of suicidal behaviors effectively particularly “Suicide Attempt” and “Family History” instances. The proposed approach, using task-specific NLP models and a multi-label classification system, captures the complexity of suicidal behaviors more effectively than traditional binary classification. However, direct comparisons with existing studies are difficult due to varying metrics and label definitions. Conclusion:This study presents a robust NLP framework for detecting suicidal behaviors in EHRs, leveraging task-specific fine-tuning of transformer-based models and a semi-automated pipeline. Despite limitations, the approach demonstrates the potential of advanced NLP techniques in enhancing the identification of suicidal behaviors. Future work should focus on model expansion and integration to further improve patient care and clinical decision-making.

Analysis of Public Sentiment Regarding the Issue of Cancelling the Revision of the 2024 Regional Election Law with NLP

The latest changes regarding the age requirements for regional head candidates in the 2024 Election by the Supreme Court have given rise to various responses from the public. This decision has become an important issue that is widely discussed on social media and mass media, where this decision has caused various reactions, both positive, negative and neutral. Sentiment analysis is important to determine public opinion on this decision. This study aims to analyze public sentiment on the Issue of Cancellation of the Revision of the 2024 Pilkada Law. This study uses the Natural Language Processing (NLP) method with the Naive Bayes algorithm. The data used is text data in the form of public opinion on the Issue of Cancellation of the Revision of the 2024 Pilkada Law collected via Twitter. This data is then processed using NLP techniques such as cleaning, tokenization, normalization, filtering and stemming. After that, public opinion on the Issue of Cancellation of the Revision of the 2024 Pilkada Law is classified into positive, negative and neutral sentiment categories. The results of the study showed that the results of the model performance evaluation using data testing produced an accuracy of 92%, so it can be concluded that this model is good enough for sentiment analysis. Accuracy shows that the model makes correct predictions on 92% of the total data, which is dominated by the neutral class.

Harnessing LLMs for Financial Forecasting: A Systematic Review of Advances in Stock Market Prediction and Portfolio Optimization

Abstract: This review paper examines the rapidly evolving landscape of Large Language Models (LLMs) in financial analysis, synthesizing recent advances and applications in this transformative field. We trace the progression from traditional natural language processing (NLP) methods to contemporary language models in financial applications, showing how these technologies are reshaping market analysis, risk assessment, fraud detection, and investment decision-making across various financial sectors. The paper offers an in-depth overview of LLM architectures and approaches used in finance, covering both general-purpose models adapted for financial tasks and specialized models tailored for industry needs. Through systematic analysis of the latest research and empirical studies, we assess the capabilities of LLMs in processing diverse financial data sources, including real-time market data, news articles, social media sentiment, earnings calls, and regulatory filings, enhancing insights and predictive accuracy. We identify key challenges in LLM implementation, such as the need for real-time data processing, high accuracy, and interpretability, crucial for trust and adoption in high-stakes contexts. Additionally, we explore emerging trends and future research directions, highlighting both the transformative potential and limitations of LLMs as they redefine analytical frameworks and decision support in finance. This review underscores the need for ongoing research to bridge gaps and fully realize LLMs’ potential in reshaping financial analysis and practices

A Bibliometric Review of Natural Language Processing Applications in Psychology from 1991 to 2023

Natural language processing (NLP) has emerged as a promising approach in psychological research. However, prior reviews often focused on specific subject areas and reported varying findings regarding the use of NLP methods. To address this gap in the literature, we conducted a comprehensive review of NLP applications in psychological research. Our study includes (1) a large-scale bibliometric review of 4,909 papers (1991–2023) and (2) a focused methodological review of the 100 most-cited articles. Results revealed exponential growth in NLP applications since 2012, with Health, Education, and Marketing as dominant topics. Sentiment analysis was the most common technique, deep learning—particularly pre-trained models—gained popularity, and automated text analysis tools were widely used due to their ease of implementation.

Scalable Transformer Accelerator with Variable Systolic Array for Multiple Models in Voice Assistant Applications

Transformer model is a type of deep learning model that has quickly become fundamental in natural language processing (NLP) and other machine learning tasks. Transformer hardware accelerators are usually designed for specific models, such as Bidirectional Encoder Representations from Transformers (BERT), and vision Transformer models, like the ViT. In this study, we propose a Scalable Transformer Accelerator Unit (STAU) for multiple models, enabling efficient handling of various Transformer models used in voice assistant applications. Variable Systolic Array (VSA) centralized design, along with control and data preprocessing in embedded processors, enables matrix operations of varying sizes. In addition, we propose an efficient variable structure and a row-wise data input method for natural language processing where the word count changes. The proposed scalable Transformer accelerator accelerates text summarization, audio processing, image search, and generative AI used in voice assistance.

LCD benchmark: long clinical document benchmark on mortality prediction for language models.

The application of natural language processing (NLP) in the clinical domain is important due to the rich unstructured information in clinical documents, which often remains inaccessible in structured data. When applying NLP methods to a certain domain, the role of benchmark datasets is crucial as benchmark datasets not only guide the selection of best-performing models but also enable the assessment of the reliability of the generated outputs. Despite the recent availability of language models capable of longer context, benchmark datasets targeting long clinical document classification tasks are absent. To address this issue, we propose Long Clinical Document (LCD) benchmark, a benchmark for the task of predicting 30-day out-of-hospital mortality using discharge notes of Medical Information Mart for Intensive Care IV and statewide death data. We evaluated this benchmark dataset using baseline models, from bag-of-words and convolutional neural network to instruction-tuned large language models. Additionally, we provide a comprehensive analysis of the model outputs, including manual review and visualization of model weights, to offer insights into their predictive capabilities and limitations. Baseline models showed 28.9% for best-performing supervised models and 32.2% for GPT-4 in F1 metrics. Notes in our dataset have a median word count of 1687. Our analysis of the model outputs showed that our dataset is challenging for both models and human experts, but the models can find meaningful signals from the text. We expect our LCD benchmark to be a resource for the development of advanced supervised models, or prompting methods, tailored for clinical text.

Multitextuality in RPGs: a ludonarrative synergy model for video game text analysis

The article presents the textual potential of games and distinguishes between the types of texts in text-heavy video games. Text types are categorized according to the diegetic/non-diegetic situation vis-à-vis the game world and primary text function. It is assumed that role-playing games (RPGs) such as Divinity: Original Sin 2 (Larian Studios, 2017) encompass a greater volume of textual material. The proposed corpus analysis model is based on the concept of ludonarrative synergy, text and discourselinguistics, and methods of natural language processing (NLP) and computational linguistics. Two-level text analysis using Python examines the validity of categorizing game texts according to an extended diegetic/nondiegeticmodel.

Perceived unmet needs and impact on quality of life of patients living with advanced bladder cancer and their caregivers: results of a social media listening study conducted in five European countries

BackgroundAdvanced bladder cancer (aBC) is a significant health concern in Europe and has a poor prognosis. Patients with aBC face numerous unmet needs and challenges that significantly impact their quality of life (QoL). This study aims to analyze social media data from five European countries to address gaps in our understanding of the unmet needs, challenges, and impact on QoL in European patients with aBC and their caregivers.MethodsThis retrospective, real-world study includes public social media posts geolocated in France, Italy, Germany, Spain, and the UK, posted between October 2017 and January 2022. To enhance the filtering process, natural language processing methods and specific algorithms were used to remove irrelevant content and retain posts from patients and caregivers. QoL impacts were identified using a deep-learning algorithm, followed by qualitative analysis. Unmet needs were analyzed via annotation of messages and the saturation method.ResultsA total of 1670 posts from 1396 users (699 posts from 546 patients and 971 posts from 850 caregivers) discussing aBC in 91 publicly available online sources were identified. Half of patients were male (n = 272, 49.8%) while more caregivers were female (n = 474, 55.8%), with an average age of 58.2 and 35.2 years, respectively. Patients and caregivers expressed an impact on QoL, wherein 40.0% (558/1396) of users mentioned at least an impact on one aspect of QoL. Among those, 56.8% (317/558) and 48.6% (271/558) of users expressed physical and psychological challenges, respectively. Most unmet needs identified belonged to two main categories: transversal, i.e., arising throughout the patient’s care pathway (307/1092 [28.1%]), and disease specific (295/1092 [27.0%]). Main challenges included worsening of the disease (n = 141, 12.9%), psychological impact (n = 112, 10.3%), and need to share experiences and seek support (n = 94, 8.6%).ConclusionsThis social media listening study demonstrated the profound emotional and physical burden on patients with aBC and their caregivers, and a genuine need for support and an outlet to discuss their challenges, particularly in terms of managing the illness. These results underscore the importance of enhancing education for both patients and caregivers and the necessity for more effective systemic cancer therapies and better palliative care alternatives.

Artificial intelligence-enabled social media listening to inform early patient-focused drug development: perspectives on approaches and strategies.

This article examines the opportunities and benefits of artificial intelligence (AI)-enabled social media listening (SML) in assisting successful patient-focused drug development (PFDD). PFDD aims to incorporate the patient perspective to improve the quality, relevance, safety, and efficiency of drug development and evaluation. Gathering patient perspectives to support PFDD is aided by the participation of patient groups in communicating their treatment experiences, needs, preferences, and priorities through online platforms. SML is a method of gathering feedback directly from patients; however, distilling the quantity of data into actionable insights is challenging. AI-enabled methods, such as natural language processing (NLP), can facilitate data processing from SML studies. Herein, we describe a novel, trainable, AI-enabled, SML workflow that classifies posts made by patients or caregivers and uses NLP to provide data on their experiences. Our approach is an iterative process that balances human expert-led milestones and AI-enabled processes to support data preprocessing, patient and caregiver classification, and NLP methods to produce qualitative data. We explored the applicability of this workflow in 2 studies: 1 in patients with head and neck cancers and another in patients with esophageal cancer. Continuous refinement of AI-enabled algorithms was essential for collecting accurate and valuable results. This approach and workflow contribute to the establishment of well-defined standards of SML studies and advance the methodologic quality and rigor of researchers contributing to, conducting, and evaluating SML studies in a PFDD context.

Enhancing zero-shot relation extraction with a dual contrastive learning framework and a cross-attention module

Zero-shot relation extraction (ZSRE) is essential for improving the understanding of natural language relations and enhancing the accuracy and efficiency of natural language processing methods in practical applications. However, the existing ZSRE models ignore the importance of semantic information fusion and possess limitations when used for zero-shot relation extraction tasks. Thus, this paper proposes a dual contrastive learning framework and a cross-attention network module for ZSRE. First, our model designs a dual contrastive learning framework to compare the input sentences and relation descriptions from different perspectives; this process aims to achieve better separation between different relation categories in the representation space. Moreover, the cross-attention network of our model is introduced from the computer vision field to enhance the attention paid by the input instance to the relevant information of the relation description. The experimental results obtained on the Wiki-ZSL and FewRel datasets fully demonstrate the effectiveness of our approach.

Decoding the Digital Pulse: Bibliometric Analysis of 25 Years in Digital Health Research Through the Journal of Medical Internet Research.

As the digital health landscape continues to evolve, analyzing the progress and direction of the field can yield valuable insights. The Journal of Medical Internet Research (JMIR) has been at the forefront of disseminating digital health research since 1999. A comprehensive network analysis of JMIR publications can help illuminate the evolution and trends in digital medicine over the past 25 years. This study aims to conduct a detailed network analysis of JMIR's publications to uncover the growth patterns, dominant themes, and potential future trajectories in digital health research. We retrieved 8068 JMIR papers from PubMed using the Biopython library. Keyword metrics were assessed using accuracy, recall, and F1-scores to evaluate the effectiveness of keyword identification from Claude 3 Opus and Gemini 1.5 Pro in addition to 2 conventional natural language processing methods using key bidirectional encoder representations from transformers. Future trends for 2024-2026 were predicted using Claude 3 Opus, Google's Time Series Foundation Model, autoregressive integrated moving average, exponential smoothing, and Prophet. Network visualization techniques were used to represent and analyze the complex relationships between collaborating countries, paper types, and keyword co-occurrence. JMIR's publication volume showed consistent growth, with a peak in 2020. The United States dominated country contributions, with China showing a notable increase in recent years. Keyword analysis from 1999 to 2023 showed significant thematic shifts, from an early internet and digital health focus to the dominance of COVID-19 and advanced technologies such as machine learning. Predictions for 2024-2026 suggest an increased focus on artificial intelligence, digital health, and mental health. Network analysis of JMIR publications provides a macroscopic view of the evolution of the digital health field. The journal's trajectory reflects broader technological advances and shifting research priorities, including the impact of the COVID-19 pandemic. The predicted trends underscore the growing importance of computational technology in future health care research and practice. The findings from JMIR provide a glimpse into the future of digital medicine, suggesting a robust integration of artificial intelligence and continued emphasis on mental health in the postpandemic era.

Getting into bed with embeddings? A comparison of collocations and word embeddings for corpus-assisted discourse analysis

This paper discusses two approaches for identifying lexical patterns in discourse, namely the corpus linguistic method of collocation analysis and the natural language processing method of word embeddings. While both approaches can identify lexical patterns, they approach the task with different underlying frameworks, and the extent to which their results resemble one another has not been directly compared. This study uses two corpora, five collocation measures, and two word embedding algorithms to generate such comparisons. Results generally support the notion that many word pairs with similar embeddings are collocates, and that, to a lesser extent, many collocates have similar word embeddings. However, a major difference is that word pairs with similar embeddings do not need to co-occur often, or at all. Moreover, systematic differences in the kinds of words highlighted between the two word embedding algorithms were found and are discussed.

Abstract 4140118: Identification of Obstructive and Non-Obstructive Hypertrophic Cardiomyopathy Patients Using Natural Language Processing in a Large Integrated Healthcare System

Introduction: Accurately identifying and characterizing patients with hypertrophic cardiomyopathy (HCM) is critical for population management and care optimization. Research Question: To develop natural language processing (NLP) algorithms to identify and characterize obstructive (oHCM) and non-obstructive (nHCM) HCM patients directly from echocardiograms, and to compare with the presence or absence of HCM-related diagnosis codes. Methods: We developed and validated NLP algorithms to identify HCM from all adult (age≥18yrs) echocardiograms performed from 2010-2019 in Kaiser Permanente Northern CA (KPNC), capturing measures of any HCM, HCM subtype, hypertrophy subtype, septal and posterior LV wall thickness, resting and stress/Valsalva LVOT gradients, and systolic anterior motion. We developed a rules-based algorithm (following AHA/ACC criteria) to classify patients as having HCM, including oHCM or nHCM subtypes, and possible HCM (defined as wall thickness ≥2cm without other criteria meeting an HCM definition). We evaluated the presence of HCM-related ICD-9/10 diagnosis codes among patients classified as HCM/non-HCM from echocardiograms using NLP, and linked baseline demographics and clinical parameters from our integrated electronic medical record. Results: Among 472,405 adults with echocardiograms, we identified 2,892 patients with HCM based upon NLP-derived measures (all NLP measures achieved >95% positive predictive value and >95% negative predictive value), including 1,585 (55%) with oHCM, 1,145 (40%) with nHCM, and 162 (6%) which could not be classified (Figure). Among those 2,892 patients, 1,283 did not have any associated HCM ICD-9/10 diagnosis codes (Table). Among 469,513 patients with no identified HCM from NLP-based algorithms, HCM ICD-9/10 diagnosis codes existed in 1,567 patients (Table). We also identified 4,593 patients with possible HCM by NLP, only 4.5% of whom had an associated HCM code. Among confirmed HCM patients by NLP, oHCM patients were slightly older (66 vs 61 yrs), more likely female (53% vs 43%), had similar mean septal wall thickness (1.7cm vs 1.7cm), but were more likely to have a septal hypertrophy subtype (46% vs 28%) compared to nHCM patients. Conclusions: Echocardiogram-based NLP methods can improve the identification of and care for HCM patients. Many patients with possible HCM may be underdiagnosed, representing an opportunity for quality improvement.

Protein-Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools.

The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to significant biological breakthroughs. For example, the study of PPIs involved in the human:SARS-CoV-2 viral infection mechanism aided in the development of SARS-CoV-2 vaccines. Though several databases exist for the manual curation of PPI networks, text mining methods have been routinely demonstrated as useful alternatives for newly studied or understudied species, where databases are incomplete. Here, the relationship extraction performance of several open-source classical text processing, machine learning (ML)-based natural language processing (NLP), and large language model (LLM)-based NLP tools was compared. Overall, our results indicated that networks derived from classical methods tend to have high true positive rates at the expense of having overconnected networks, ML-based NLP methods have lower true positive rates but networks with the closest structures to the target network, and LLM-based NLP methods tend to exist between the two other approaches, with variable performances. The selection of a specific NLP approach should be tied to the needs of a study and text availability, as models varied in performance due to the amount of text provided.

Will Public Health Emergencies Affect Compensatory Consumption Behavior? Evidence from Emotional Eating Perspective.

This research examines the correlation between the COVID-19 pandemic and the desire to engage in compensatory consuming behaviors, specifically emphasizing emotional eating as a psychological coping strategy, particularly with respect to snacks and sweets. Conducting sentiment analysis by using a Natural Language Processing (NLP) method on posts from Sina Weibo, a leading Chinese social media platform, the research identifies three distinct phases of consumer behavior during the pandemic: anxiety, escapism, and compensatory periods. These stages are marked by varying degrees of emotional eating tendencies, illustrating a psychological trajectory from initial shock to seeking comfort through food as a means of regaining a sense of normalcy and control. The analysis reveals a notable increase in posts expressing a desire for compensatory consumption of snacks and sweets in 2020 compared to 2019, indicating a significant shift towards emotional eating amid the pandemic. This shift reflects the broader psychological impacts of the crisis, offering insights into consumer behavior and the role of digital platforms in capturing public sentiment during global crises. The findings have implications for policymakers, health professionals, and the food industry, suggesting the need for strategies to address the psychological and behavioral effects of natural disasters.

CommSense: A Wearable Sensing Computational Framework for Evaluating Patient-Clinician Interactions

Quality patient-provider communication is critical to improve clinical care and patient outcomes. While progress has been made with communication skills training for clinicians, significant gaps exist in how to best monitor, measure, and evaluate the implementation of communication skills in the actual clinical setting. Advancements in ubiquitous technology and natural language processing make it possible to realize more objective, real-time assessment of clinical interactions and in turn provide more timely feedback to clinicians about their communication effectiveness. In this paper, we propose CommSense, a computational sensing framework that combines smartwatch audio and transcripts with natural language processing methods to measure selected "best-practice'' communication metrics captured by wearable devices in the context of palliative care interactions, including understanding, empathy, presence, emotion, and clarity. We conducted a pilot study involving N=40 clinician participants, to test the technical feasibility and acceptability of CommSense in a simulated clinical setting. Our findings demonstrate that CommSense effectively captures most communication metrics and is well-received by both practicing clinicians and student trainees. Our study also highlights the potential for digital technology to enhance communication skills training for healthcare providers and students, ultimately resulting in more equitable delivery of healthcare and accessible, lower cost tools for training with the potential to improve patient outcomes.

Screening for Depression Using Natural Language Processing: Literature Review.

Depression is a prevalent global mental health disorder with substantial individual and societal impact. Natural language processing (NLP), a branch of artificial intelligence, offers the potential for improving depression screening by extracting meaningful information from textual data, but there are challenges and ethical considerations. This literature review aims to explore existing NLP methods for detecting depression, discuss successes and limitations, address ethical concerns, and highlight potential biases. A literature search was conducted using Semantic Scholar, PubMed, and Google Scholar to identify studies on depression screening using NLP. Keywords included "depression screening," "depression detection," and "natural language processing." Studies were included if they discussed the application of NLP techniques for depression screening or detection. Studies were screened and selected for relevance, with data extracted and synthesized to identify common themes and gaps in the literature. NLP techniques, including sentiment analysis, linguistic markers, and deep learning models, offer practical tools for depression screening. Supervised and unsupervised machine learning models and large language models like transformers have demonstrated high accuracy in a variety of application domains. However, ethical concerns related to privacy, bias, interpretability, and lack of regulations to protect individuals arise. Furthermore, cultural and multilingual perspectives highlight the need for culturally sensitive models. NLP presents opportunities to enhance depression detection, but considerable challenges persist. Ethical concerns must be addressed, governance guidance is needed to mitigate risks, and cross-cultural perspectives must be integrated. Future directions include improving interpretability, personalization, and increased collaboration with domain experts, such as data scientists and machine learning engineers. NLP's potential to enhance mental health care remains promising, depending on overcoming obstacles and continuing innovation.