Analysis Of Textual Data Research Articles

Patient Health Record Protection Beyond the Health Insurance Portability and Accountability Act: Mixed Methods Study

Background The security and privacy of health care information are crucial for maintaining the societal value of health care as a public good. However, governance over electronic health care data has proven inefficient, despite robust enforcement efforts. Both federal (HIPAA [Health Insurance Portability and Accountability Act]) and state regulations, along with the ombudsman rule, have not effectively reduced the frequency or impact of data breaches in the US health care system. While legal frameworks have bolstered data security, recent years have seen a concerning increase in breach incidents. This paper investigates common breach types and proposes best practices derived from the data as potential solutions. Objective The primary aim of this study is to analyze health care and hospital breach data, comparing it against HIPAA compliance levels across states (spatial analysis) and the impact of the Omnibus Rule over time (temporal analysis). The goal is to establish guidelines for best practices in handling sensitive information within hospitals and clinical environments. Methods The study used data from the Department of Health and Human Services on reported breaches, assessing the severity and impact of each breach type. We then analyzed secondary data to examine whether HIPAA’s storage and retention rule amendments have influenced security and privacy incidents across all 50 states. Finally, we conducted a qualitative analysis of textual data from vulnerability and breach reports to identify actionable best practices for health care settings. Results Our findings indicate that hacking or IT incidents have the most significant impact on the number of individuals affected, highlighting this as a primary breach category. The overall difference-in-differences trend reveals no significant reduction in breach rates (P=.50), despite state-level regulations exceeding HIPAA requirements and the introduction of the ombudsman rule. This persistence in breach trends implies that even strengthened protections and additional guidelines have not effectively curbed the rising number of affected individuals. Through qualitative analysis, we identified 15 unique values and associated best practices from industry standards. Conclusions Combining quantitative and qualitative insights, we propose the “SecureSphere framework” to enhance data security in health care institutions. This framework presents key security values structured in concentric circles: core values at the center and peripheral values around them. The core values include employee management, policy, procedures, and IT management. Peripheral values encompass the remaining security attributes that support these core elements. This structured approach provides a comprehensive security strategy for protecting patient health information and is designed to help health care organizations develop sustainable practices for data security.

Design and Implementation of a Personalized Text-Based Recommendation System

Abstract: We all know that in the competitive & ever evolving Age of content -less is more and relevancy increases engagementwhich should be focal on UX. In this paper, we describe the designing and implementation of a text-based recommendation system that specializes in offering automatic personalized recommendations for textual content-dependent on user preferences, and behavior. Analyze text data and receive tailored, contextual recommendations using Natural Language Processing (NLP) and machine learning methods. This system combines collaborative, content-based and hybrid filtering techniques along with adaptive learning to improve the accuracy of recommendations over time as user interests change. Analysis of real-world datasets indicates that the system dramatically enhances recommendation precision and user satisfaction. The results highlight the promise of tailored recommendations in areas like commerce and digital media to online education, primarily filtering great volumes of information for a more customized experience

Bridging sentiment and finance: An exploration of BOD statements and bank ratios in Jordan

Purpose: This study explores the intricate interplay between sentiment extracted from Board of Directors (BOD) statements and the traditional financial metrics of banks. It investigates the relationship between a bank’s financial performance, measured by various financial ratios, and the sentiment expressed in BOD statements.Design/methodology/approach: Drawing from a dataset encompassing BOD statements from 15 Jordanian banks spanning 2017 to 2021, sentiment analysis techniques are employed to derive sentiment scores. These scores are then juxtaposed with principal components distilled from financial metrics to examine their predictive relationship.Findings: Preliminary findings reveal significant non-linear patterns between Hu and Liu’s (2004) sentimental analysis (Lui_sen), the Data Science Lab’s (n.d.) multilingual sentiment tool (multi_sen), and the principal components. A subsequent random-effects panel regression further elucidates these relationships, highlighting the significant influence of specific principal components on sentiment scores. This study provides a compelling case for integrating sentiment analysis with traditional financial indicators, offering a more comprehensive toolkit for evaluating bank performance.Research limitations/implications: Future research should explore broader datasets across multiple sectors, to validate the generalisability of the findings beyond the banking sector in Jordan.Practical implications: This research holds implications for investors, analysts, and stakeholders by combining sentiment analysis with traditional financial metrics. It offers deeper insights into the health of banks and the intentions of management. This integrated approach allows stakeholders to make informed decisions and mitigate risks based on a more comprehensive view of banks' financial situations. Additionally, the possibility of real-time sentiment analysis enables the dynamic monitoring of banking institutions' financial health as updated data becomes available.Social implications: The study’s results are valuable to users of financial information, aiding the Association of Banks and the Central Bank of Jordan in formulating policies for the banking sector. Auditors, consultants, and regulatory bodies in Jordan can also benefit from these insights.Originality/value: This is the first study exploring the relationship between sentiment extracted from BOD statements and traditional financial metrics in Jordan. It provides novel evidence of this relationship, contributing to the literature on the integration of textual data analysis and financial performance.

A Study on the Characteristics of Real Estate Education in Korean Universities through Text Mining-based Curriculum Analysis

Text mining is a method of analyzing text data to derive the characteristics or status of a described object. Since an educational program's curriculum reflects the content and goals it aims to teach, text analysis of the curriculum can reveal the characteristics of the program. This study employs text mining techniques to analyze the curricula of real estate education programs at Korean universities and proposes strategies for future development. The paper presents three main findings: First, by analyzing the curricula of universities in the UK and US that offer real estate education, the study identifies the unique characteristics of each country’s approach, reaffirming previously identified differences between them. Second, real estate education at Korean universities features an eclectic curriculum that incorporates elements of both UK and US real estate education, resulting in diverse curricular offerings across institutions. Lastly, for individual Korean universities, the study provides guidelines for the development of their departmental education by indicating which UK or US universities have the most similar or different curricula.

Emergence of psycho-cognitive and socio-political perspectives during the dashboard appropriation process: The case of industrial SMEs in Morocco

The study aims to explore how the dashboard, initially designed to provide a rational view of organizational complexity, is appropriated by organizational actors in Moroccan Small and Medium Enterprises (SMEs). The process of appropriation is iterative and complex, involving modifications to the artifact dimension of the tool and adaptations by user-actors in their cognitive, behavioral, and social dynamics. The research highlights the psycho-cognitive and socio-political factors influencing the use of the dashboard as a key management control tool. The motivation behind this study is to understand why such a tool can lead to disruptions, resistance to change, contextualization issues, and adoption problems within organizations, rather than fulfilling its initial purpose of rationalizing processes and standardizing behaviors. The research adopts a qualitative approach using the case study method, specifically multiple embedded case studies. The sample consists of three Moroccan SMEs within the industrial food sector. We deemed it appropriate to use a textual data analysis approach, selecting two methods for this purpose: lexical analysis and thematic analysis. The qualitative data analysis was conducted using NVivo 12 software. The findings indicate that the Pre-Appropriation phase creates a context that balances technical and political factors to reduce resistance among stakeholders. The subsequent Original Appropriation Phase involves a collective decision to adopt the dashboard, which can lead to either compliant integration or deviations that enhance user adaptation. Lastly, the Reappropriation phase challenges established routines, potentially resulting in either meaningful transformations or superficial engagement with the dashboard's features. This study demonstrates that the iterative process of dashboard appropriation in Moroccan SMEs, influenced by psychocognitive and sociopolitical factors, can result in resistance, adoption challenges, and unanticipated organizational adaptations and transformations.

Insights From Community Organizations Collaborating With Government and Academia to Foster Health Literacy.

The COVID-19 pandemic disproportionately impacted ethnic minority and other underserved communities, prompting the federal government to fund community-led health literacy initiatives. This evaluation shares challenges and lessons learned, and identifies best practices from community-based organizations (CBOs) partnering with academia and local government to support health literacy programming. In 2023, 20 representatives from 12 CBOs in Southern California responded to an interviewer-administered survey, including open-ended questions about lessons learned. We analyze text data to identify key themes. CBOs expressed a need for additional financial resources and technical assistance, including grantsmanship training, access to in-language resources, support to build and use in-house evaluation systems, and greater university oversight for student collaborators. Respondents highlighted the importance of community engagement efforts to build trust and enhance the uptake of programming. They also stressed the need for specialized training of community health workers engaged in health programs and the utilization of population sub-group data to inform policy and funding development to better serve communities' needs. CBOs are eager to partner with government and academia and acknowledge the need for targeted support and resources that can be facilitated through multisectoral partnerships. We offer recommendations and actionable strategies for funders and academia to create and improve equitable partnerships with CBOs. Partnerships that address the needs and systemic barriers experienced by ethnic minority communities will support local public health goals while fostering resilience and health equity.

Theorizing Far-Right Foreign Policy: Insights From Germany

What is the far-right’s foreign policy outlook? Although International Relations scholarship has provided important insights into the foreign policy preferences of far-right actors, it has predominantly analysed these political actors under the label of populism and focused on the effects of populism on foreign policy positions. Consequently, we lack a clear understanding of the impact of far-right ideology on foreign policy beliefs and preferences. This article provides a theorization of far-right foreign policy by deriving its key characteristics from far-right ideology. It tests this theoretical framework through a comparative analysis of the foreign policy preferences of the populist radical-right Alternative for Germany and the extreme-right, Neo-Nazi National Democratic Party of Germany. The comparative analysis of primary textual data reveals a shared far-right foreign policy outlook characterized by (1) ultra-nationalism, (2) group-based enmity, (3) authoritarianism, (4) revisionism and reactionism and (5) producerist-nationalistic economic positions, but also some important variations in the pursuit of these positions.

Disinformation, Fakes and Propaganda Identifying Methods in Online Messages Based on NLP and Machine Learning Methods

A new method of propaganda analysis is proposed to identify signs and change the dynamics of the behaviour of coordinated groups based on machine learning at the processing disinformation stages. In the course of the work, two models were implemented to recognise propaganda in textual data - at the message level and the phrase level. Within the framework of solving the problem of analysis and recognition of text data, in particular, fake news on the Internet, an important component of NLP technology (natural language processing) is the classification of words in text data. In this context, classification is the assignment or assignment of textual data to one or more predefined categories or classes. For this purpose, the task of binary text classification was solved. Both models are built based on logistic regression, and in the process of data preparation and feature extraction, such methods as vectorisation using TF-IDF vectorisation (Term Frequency – Inverse Document Frequency), the BOW model (Bag-of-Words), POS marking (Part-Of-Speech), word embedding using the Word2Vec two-layer neural network, as well as manual feature extraction methods aimed at identifying specific methods of political propaganda in texts are used. The analogues of the project under development are analysed the subject area (the propaganda used in the media and the basis of its production methods) is studied. The software implementation is carried out in Python, using the seaborn, matplotlib, genism, spacy, NLTK (Natural Language Toolkit), NumPy, pandas, scikit-learn libraries. The model's score for propaganda recognition at the phrase level was obtained: 0.74, and at the message level: 0.99. The implementation of the results will significantly reduce the time required to make the most appropriate decision on the implementation of counter-disinformation measures concerning the identified coordinated groups of disinformation generation, fake news and propaganda. Different classification algorithms for detecting fake news and non-fakes or fakes identification accuracy from Internet resources ana social mass media are used as the decision tree (for non-fakes identification accuracy 0.98 and fakes identification accuracy 0.9903), the k-nearest neighbours (0.83/0.999), the random forest (0.991/0.933), the multilayer perceptron (0.9979/0.9945), the logistic regression (0.9965/0.9988), and the Bayes classifier (0.998/0.913). The logistic regression (0.9965) the multilayer perceptron (0.9979) and the Bayesian classifier (0.998) are more optimal for non-fakes news identification. The logistic regression (0.9988), the multilayer perceptron (0.9945), and k-nearest neighbours (0.999) are more optimal for identifying fake news identification.

Automatic Processing of Bibliometric Data: Newest Literature Analysis Algorithms

Automated processing of bibliometric data has become an integral element in understanding and measuring the impact of scientific research as well as trends in the academic literature. The purpose of this study was to evaluate and compare current literature analysis algorithms to identify advances in automated processing of bibliometric data. This study explores the use of the latest algorithms in literature analysis which includes identification of research themes, research network analysis, citation impact evaluation, and development of literature recommendation algorithms. This research exposes innovative applications that utilize the latest technologies in text processing and data analysis to assist researchers, libraries, and information professionals in revealing deep insights from the latest scientific literature. The results of this study illustrate that the automated processing of bibliometric data with the latest algorithms paves the way for a deeper understanding of the development of scientific research, its impact in the scientific community as well as improving bibliometric analysis methods and tools by integrating the latest algorithms, so that scientific research and trends can be identified more accurately and relevantly. The study could use more sophisticated multifaceted techniques to analyze simple productivity measures based on article citations. This research is also able to recognize and describe the development of new science and technology.

Natural Language Processing Risk Assessment Application Developed for Marble Quarries

In this study, by using the texts describing the hazards and precautions taken during text mining, the necessary processes were carried out to first estimate the probability value and severity value of the risk and then calculate the risk values by Natural Language Processing analysis. In order to be used within the scope of the study, two data sets were generated from the data in the risk assessment report prepared by applying the L-type matrix risk assessment in marble quarries between 2015 and 2021. Stochastic Gradient Descent (SGD) was used for classification and prediction by analyzing text data. One data set was used to analyze the probability value of the risk and the other was used to analyze the severity value of the risk. In light of the results, when a text containing hazard and precaution information was entered, a system was developed that analyzed this text, estimated the probability and severity values, and calculated the risk assessment score. The application of the SGD algorithm to learning models developed on text data yielded an accuracy rate of 91.2% in the risk probability data set and 97.5% in the risk severity data set. The results indicated that the models were capable of conducting automatic risk assessment on text data and of effectively predicting the requisite probability and severity values. Due to the high accuracy rates obtained during the study, this risk assessment software was recommended for use in marble quarries.

Интеллектуальная обработка текстовой информации: обзор автоматизированных методов суммаризации

Interest in innovative technological strategies and modern digital tools has increased significantly due to the need to manage large amounts of unstructured data. This paper reviews current paradigms and services for automated summarization, developed based on interdisciplinary research in linguistics, computer technologies, and artificial intelligence. It focuses on syntactic and lexical techniques employed by neural network models for text compression. The paper presents performance examples of such AI-powered services as QuillBot, Summate.it, WordTune, SciSummary, Scholarcy, and OpenAI ChatGPT. The contemporary automated models proved effective in using extractive and abstractive methods to generate summaries of varying quality and length. The extractive approach relies on identifying the most significant sentences from the original text, while abstractive algorithms create new sentence structures that preserve the main idea of the original content. Automated summarizers effectively utilize text compression techniques that are inherent to human approach to text processing, e.g., they exclude redundant information, simplify complex structures, and generalize data. These technologies provide high accuracy and coherence in the generated summaries, though each summarization model has its limitations. Optimal results depend on the specifics of the task at hand: extractive models provide brevity and precision while abstractive ones allow for deeper semantic processing. Automated summarization is becoming an important tool in various fields that require effective analysis and processing of large text data.

Comparison of Machine Learning Algorithms in Public Sentiment Analysis of TAPERA Policy

The rapid development of information technology has changed the way people interact and express their opinions on public policies, including the People's Housing Savings (Tapera) policy in Indonesia. People now primarily express their views openly on social media platforms like Twitter, generating a substantial amount of text data for analysis to understand public sentiment. However, the main challenge in this sentiment analysis is determining the most effective machine learning algorithm for classifying public opinion with high accuracy. This study aims to compare the performance of three machine learning algorithms, namely Naïve Bayes, Support Vector Machine, and Random Forest, in analyzing public sentiment towards the Tapera policy. This study analyzes public comment data obtained from Twitter. We measure the accuracy of each algorithm to determine its optimal performance in sentiment classification. The research method consists of several stages, starting with data collection, text preprocessing to clean and prepare data, and then applying the three algorithms to analyze sentiment. The results showed that Naïve Bayes had the highest accuracy of 69.17%, followed by Support Vector Machine with an accuracy of 68.42%, and Random Forest with an accuracy of 66.17%. This shows that Naïve Bayes is the most effective algorithm to use in sentiment analysis of public comments related to the Tapera policy, especially in the context of complex text data from social media. The conclusion of this study is that Naïve Bayes is superior in classifying public sentiment towards the Tapera policy compared to Support Vector Machine and Random Forest. As a result, this study makes a significant contribution to selecting the most appropriate machine learning algorithm for public sentiment analysis towards public policy, which in turn can help the government understand and respond to public perceptions more effectively.

BERTopic과 색채분석 기반 감성 추출을 이용한 관광지 추천 시스템 개발

This study aims to develop a personalized tourist destination recommendation system by analyzing the color and emotional factors of destination images. It evaluated and analyzed the emotional responses and color characteristics conveyed by images of natural resorts and urban tourist sites. A survey was conducted to collect users' emotional responses to these images, and a BERTopic model based on BERT was used to analyze text data from social media. The survey results provided a clear understanding of how users react to specific colors and emotional factors. For natural resorts, Green(G) was the most associated color, followed by Green-Yellow(GY) and Blue(B). In the case of urban tourist sites, Yellow-Red(YR) and Yellow(Y) were most commonly associated, followed by Blue-Green(BG) and Blue(B). Additionally, during the system's execution, the Bhattacharyya distance was used to measure color similarity between images, displaying images that match the user's emotions. Therefore, this study developed a recommendation system that considers the impact of destination images on users' emotions, proposing a new approach to better reflect users' emotional needs.

Named entity recognition and its role in unstructured data analysis

In today's digital world, where vast amounts of unstructured data are generated every day, the ability to efficiently process this information is key for many industries. Unstructured data, which includes text files, emails, video, audio, images, and other forms of media, is the bulk of digital data and requires specialized tools to analyze it. Natural Language Processing (NLP) and Named Entity Recognition (NER) are two key technologies that enable the transformation of unstructured data into structured information that can be used for a variety of applications. Natural Language Processing enables machines to understand, interpret, manipulate and generate human language, opening up possibilities for deep analysis of textual data. This includes identifying key words, phrases, themes, and emotional nuances in texts. NER, as an important component of Natural Language Processing, specializes in identifying and classifying named entities in the text into certain categories, such as names of persons, organizations, locations, dates, times, and others. This allows you to automate the processes of sorting, categorizing and analyzing information. However, working with Natural Language Processing and Named Entity Recognition faces a number of challenges. The large volume and variety of data make it difficult to collect, store and analyze it. Lack of standardization can lead to problems with interoperability and integration of different data sources. In addition, there are challenges related to the recognition of named entities, in particular, distinguishing between the same names belonging to different persons and understanding the context in which the names are used. Despite these challenges, the outlook for Natural Language Processing and Named Entity Recognition looks bright, with continued innovations in artificial intelligence and machine learning promising to improve the accuracy and efficiency of these technologies in the future.

How do medical students' expectations shape their experiences of well-being programmes?

Medical schools have a duty of care to support their students' health and well-being. Student support studies have tended to focus on outcomes in respect of effectiveness and satisfaction. In contrast, little is known about how student expectations of support may shape their experiences and engagement with support mechanisms, as well as the relationships students have with those offering support (including the institution itself). To address this gap in knowledge, we explore how medical students' expectations of student support systems shape, and are shaped by, institutional rules and processes. We employed a qualitative case study approach using an institutional ethnography informed methodology. Our context was a medical school that provides a well-advertised, formal institutional support system where students are assigned a personal tutor. Data collection included interviews with medical students (n = 13) plus document analysis (public facing artefacts and internal policies/guidelines related to the support system). We applied the lens of psychological contract theory to guide abductive analysis of interview and text data. Students expected a strong support system to be provided by the medical school and the personal tutors. However, their experiences did not always align with their expectations. Some felt excluded by the system while others regarded the relationship with their personal tutor as more transactional than relational. Where their expectations were unmet, students responded by reducing their engagement with the formal support system and creating their own peer-support network that supplemented existing formal support. Student expectations matter in shaping their experiences of support systems. Where expectations are unmet, students may disengage and/or find alternatives. This may be easier for some students compared with others. More understanding of the relationship between expectations and engagement with support can inform the development of institutional support structures that meet the needs of all students across time.

What is Polarization in the Social Sciences? A Scoping Review of Reviews

The word polarization has gained notoriety both in journalistic headlines and academic publications to explain the social and political conflicts of recent years. Despite its relevance, this concept is used indiscriminately, so its meaning is not always clear. Consequently, this research aims to provide an overview of polarization in the social sciences, in terms of characteristics of polarization, theories, and associated variables that support its academic use. A review of reviews was carried out using the PRISMA methodology. 56 publications from different disciplines and databases were analyzed. The methodological quality of the publications was evaluated using the AMSTAR2 and SANRA instruments. The publications were analyzed by statistical analysis of textual data. Based on these analyses, definitions of three classes and 19 subclasses of polarization were defined. The quality of the reviews was determined, and the variables and theories associated with the phenomenon were specified. Most of the reviews are narratives, which show little methodological systematicity. Similarly, most of the reviews relate to political polarization, and only five variables were found to be inversely related to polarization, which shows how little depolarization has been studied.

LDAPrototype: a model selection algorithm to improve reliability of latent Dirichlet allocation

Latent Dirichlet allocation (LDA) is a popular method for analyzing large text corpora, but it suffers from instability due to its reliance on random initialization. This results in different outcomes for replicated runs, hindering reproducibility. To address this, we introduce LDAPrototype, a new approach for selecting the most representative LDA run from multiple replications on the same dataset. LDAPrototype enhances the reliability of LDA conclusions by ensuring greater similarity between replications compared to traditional LDA runs or models chosen based on perplexity or NPMI. A key feature of LDAPrototype is its use of a novel model similarity measure called S-CLOP (Similarity of multiple sets by Clustering with LOcal Pruning). It is based on topic similarities, for which we compare the usage of measures like the thresholded Jaccard coefficient, cosine similarity, Jensen-Shannon divergence, and rank-biased overlap. The effectiveness of LDAPrototype is demonstrated through its application to six real datasets, including newspaper articles and tweets. The results show improved reproducibility and reliability in topic modeling outcomes. LDAPrototype’s approach is noteworthy for its practical applicability, comprehensibility, ease of implementation, and computational efficiency. Furthermore, the algorithm’s concept can be generalized to other topic modeling procedures that characterize topics through word distributions, making it a versatile tool in text data analysis.

Assessment and Evaluation Methods for 21st Century Education: Measuring What Matters

In modern education, not only teaching methods, but also assessment methods play an important role. An adequate assessment system is essential to the development of skills that are necessary for successful careers of applicants. Therefore, the purpose of the research is the analysis of modern assessment methods in the educational process and their significance for the formation of 21st century skills. The PRISMA approach was used to search for relevant materials. Google Scholar was chosen as the main database for literature search. Works written in English or with English annotations were taken into analysis. 10 criteria for the inclusion of literature were developed, which related to the relevance, practical importance and scientific novelty of the research. Therefore, 40 positions were selected. The study used the coding method for the systematic analysis of textual data. Hence, the results showed that the main approaches to assessment in modern education are summative and formative assessment. Summative assessment summarizes student achievement after the learning process is complete, while formative assessment improves learning outcomes in real time through feedback. Special attention is paid to the active use of digital technologies, such as computer adaptive testing, e-portfolios and platforms for online testing. The conclusions emphasize that innovative assessment should contribute to the development of key skills of the 21st century. The development of digital literacy is especially important, as it allows all participants of the study to actively use technologies and understand them.

The urban in ecology: a quantitative textual analysis of the scientific literature over a century

Many authors have described ‘urban ecology’ as a subfield of ecology that emerged in the 1990s and has expanded exponentially. We propose to take a step further and analyse the expansion of the ‘urban’ in ecology with a novel quantitative approach, with the aim to better understand the relationship between ecology and the urban. Previous quantitative assessments of the urban in ecology have focused on short to medium time spans (5 to 40 years) and on research coined as ‘urban ecology’, and have rarely considered the content of publications (e.g., vocabulary and topics) using quantitative methods. In this paper, we conduct a bibliometric assessment and an in-depth quantitative textual data analysis of a corpus of 960 articles published from 1922 to 2018 in 10 leading English-language journals in ecology and conservation biology. We address the following questions: (1) When and how have urban environments been integrated into ecological research during the past century? (2) What urban research topics have been investigated in ecology during the same period? (3) How have these research topics changed through time? Our results show that the urban was never entirely absent from publications in ecology. The quantitative analyses highlight three turning points (1970s, 1990s and 2000s) in the relationship between ecology and the urban. Moreover, they help visualize the shift from particularly scattered publications at the beginning of the period to publications characterized by a more homogeneous vocabulary, reflecting the stabilization of a research field focused on the urban in ecology.

Exploring the multifaceted vulnerabilities of female street child labor in the capital city of Bangladesh

Numerous children experience vulnerability due to their families' profound economic and socio-economic hardships. Among this demographic, females face heightened susceptibility, particularly those engaged in child labor. Dhaka, the capital city of Bangladesh, hosts a substantial population of female child laborers, compounding their precarious circumstances. Hence this study utilizes a qualitative phenomenological approach to investigate the vulnerabilities affecting these female child laborers thoroughly. A total of 25 in-depth interviews were conducted with female child laborers in Dhaka city, following a semi-structured format. NVivo 14 software was instrumental in the systematic coding and analysis of the extensive text data, enhancing the reliability and validity of the findings. This study, therefore, explores various vulnerabilities faced by female child laborers in Dhaka city, including risky health behavior, abusive behavior, sexual harassment, school dropout, unhygienic dietary habits, hazardous work conditions, and substandard living environments. Their socio-economic conditions make them susceptible to physical and mental setbacks, exploitation, and loss of dignity. The study emphasizes the necessity for comprehensive support and breaking the cycle through educational, health, and social initiatives. It offers a detailed portrayal of the living conditions of female child laborers in Dhaka city, providing valuable insights and evidence-based policy prescriptions for policymakers and Non-Government Organizations to formulate effective policies and measures to safeguard this vulnerable community.