A requirement mining framework to support complex sub-systems suppliers

In unstructured text, process of looking for patterns is called text mining. Finding particular elements in documents written in natural language is a related task of information extraction (IE). Text mining has grown as an importance in study field. It is discovery of the new and past undiscovered information by computers from a variety of hand-written sources automatically. We currently live in a time marked by extreme stress and mental illnesses. A greater number of people are committing suicide as a result of the excessive levels of stress and strain because they tend to increase the amount of people who exhibit risky inclinations. Family conflict, work dissatisfaction, health issues, etc. are common sources of stress. In the modern computing environment, people are pleased to express their opinions and sentiments on social media with friends and family members by using tools like electronic communication. The suitable text mining technique can increase efficiency and cut down on the time and effort needed to retrieve relevant information. The Natural Language Processing technique which is used to extract important information from text messages is briefly discussed and analyzed in this work.

Large-scale data on type-specific human papillomavirus (HPV) prevalence and disease burden worldwide are needed to guide cervical cancer prevention efforts. Promoting the research and application of health care big data has become a key factor in modern medical research. To examine the prevaccination prevalence of high-risk HPV (hrHPV) and type distribution by cervical cytology grade in Estonia. This cross-sectional study used text mining and the linking of data from electronic health records and health care claims to examine type-specific hrHPV positivity in Estonia from 2012 to 2019. Participants were women aged at least 18 years. Statistical analysis was performed from September 2021 to August 2022. Type-specific hrHPV positivity rate by cervical cytological grade. A total of 11 017 cases of cervical cytology complemented with data on hrHPV testing results between 2012 and 2019 from 66 451 women aged at least 18 years (mean [SD] age, 48.1 [21.0] years) were included. The most common hrHPV types were HPV16, 18, 31, 33, 51 and 52, which accounted for 73.8% of all hrHPV types detected. There was a marked decline in the positivity rate of hrHPV infection with increasing age, but the proportion did not vary significantly based on HPV type. Implementation of nonavalent prophylactic vaccination was estimated to reduce the number of women with high-grade cytology by 50.5% (95% CI, 47.4%-53.6%) and the number with low-grade cytology by 27.8% (95% CI, 26.3%-29.3%), giving an overall estimated reduction of 33.1% (95% CI, 31.7%-34.5%) in the number of women with precancerous cervical cytology findings. In this cross-sectional study, text mining and natural language processing techniques allowed the detection of precursors to cervical cancer based on data stored by the nationwide health system. These findings contribute to the literature on type-specific HPV distribution by cervical cytology grade and document that α-9 phylogenetic group HPV types 16, 31, 33, 52 and α-7 phylogenetic group HPV 18 are the most frequently detected in normal-to-high-grade precancerous lesions in Estonia.

Text produced by entrepreneurs represents a data source in entrepreneurship research on venture performance and fund-raising success. Manual text coding of single variables is increasingly assisted or replaced by computer-aided text analysis. Yet, for the development of prediction models with several variables, such dictionary-based text analysis methods are less suitable. Natural language processing techniques are an alternative; however, the implementation is more complex and requires substantial programming skills. More work is required to understand how text analytics can advance entrepreneurship research. This study hence experiments with different artificial intelligence methods rooted in Natural Language Processing and deep learning. It uses 766 business plans to train a model for the automated measurement of transaction relations, a construct which is an indicator for new technology-based firm survival. Empirical findings show that the accuracy of construct measurement can be significantly increased with automated methods and improves with larger amounts of training data. Language complexity sets limits to the precision of automated construct measurement though. We therefore recommend a hybrid approach: making use of the inherent advantages of combining automated with human coding until the amount of training data is sufficiently large to substitute the human coding completely. The study provides insights into the applicability of different text analytics methods in entrepreneurship research and points at future research potential.

The rapid expansion of metal-organic framework (MOF) literature presents both a rich resource and a significant challenge for knowledge extraction. Text mining, which enables the conversion of unstructured scientific texts into structured, machine-readable data, has emerged as a key tool for accelerating data-driven research in the MOF domain. This review traces the development of text mining approaches in MOF research, from early manual curation and rule-based methods to recent breakthroughs powered by large language model (LLM)-based automation. We discuss the foundational role of natural language processing (NLP) and machine learning (ML) techniques such as named entity recognition and vector embedding models, followed by an in-depth analysis of LLM-based frameworks that enable flexible, scalable, and context-aware information extraction. Additionally, we introduce and compare their accuracy, and explore their diverse applications-including prediction of synthesizability, materials properties, and thermal stability. We conclude with a perspective on future directions for text mining in MOF research, including its integration into interactive graphical user interfaces, autonomous laboratories, multi-agent AI systems, and multi-modal LLM frameworks that can process textual, visual, and structural information in a unified way. This review aims to provide a foundational understanding for both experimental and computational researchers interested in adopting or advancing text mining methods in the MOF field.

The fields of taxonomy and biodiversity research have witnessed an exponential growth in published literature. This vast corpus of articles holds information on the diverse biological traits of organisms and their ecologies. However, access to and extraction of relevant data from this extensive resource remain challenging. Advances in text and data mining (TDM) and Natural Language Processing (NLP) techniques offer new opportunities for liberating such information from literature. Testing and using such approaches to annotate articles in machine-actionable formats is, therefore, necessary to enable the exploitation of existing knowledge in new biology, ecology and evolution research. Here, we explore the potential of these methods to annotate and extract organismal trait data for the most diverse animal group on Earth, the arthropods. The article processing workflow uses manually curated trait dictionaries with trained NLP models to perform labelling of entities and relationships of thousands of articles. A subset of manually annotated documents facilitated the formal evaluation of the performance of the workflow in terms of entity recognition and normalisation and relationship extraction, highlighting several important technical challenges. The results are made available to the scientific community through an interactive web tool and queryable resource, the ArTraDB Arthropod Trait Database. These methodological explorations provide a framework that could be extended beyond the arthropods, where TDM and NLP approaches applied to the taxonomy and biodiversity literature will greatly facilitate data synthesis studies and literature reviews, the identification of knowledge gaps and biases, as well as the data-informed investigation of ecological and evolutionary trends and patterns.

Customer feedback in the modern era like today is mostly presented in the form of digital reviews, including customer feedback at an inn or hotel, customer feedback is very valuable data where from this data the management can find out, identify and analyze the customer experience and what they need. With customer feedback in the form of digital reviews, it will allow a lot of data that can be obtained by hotel management and will provide many benefits if the data is processed correctly. To take advantage of large text review data, a combination of data mining and natural language processing techniques was chosen to process text in depth and efficiently. Text mining in the form of creating an opinion mining model using the Naïve Bayes classification algorithm is applied to find information and measure the main sentiments expressed in the reviewed text dataset, then the application of K-Means text grouping aims to group texts and get information about the main topics discussed from the content of the review dataset text in each group . By applying the constructed sentiment analysis model, approximately 90.90% accuracy results were obtained in reading texts and measuring sentiments related to hotel customer feedback data.

To assess the quality-of-service PGRI Jawa Tengah provided to its community through its teacher training programs, it needed to understand the input from the community itself. However, analysing and gaining insight from thousands of written feedback is hard and tedious. This research aimed to extract information from training participants’ feedback using Natural Language Processing techniques. The result showed that the most recurring theme for participant feedback is “very beneficial”, “very helpful”, “satisfactory”, “thank you”, “adding insights”, and “more training like this”. This means that most participants are satisfied with the training held by PGRI Jawa Tengah and expect more of this training to be held in the future. Based on these findings, PGRI Jawa Tengah should hold more teacher training programs in the future that are similar or greater in quality than the already established training.
 Keywords: teacher training; participants feedback; text mining; natural language processing

This paper presents an approach to fake review detection, essentially for online hotel reviews, by combining the review-based approach and reviewer-based approach. Different Natural Language Processing techniques such as tokenization, lemmatization, vectorization, etc. are used to extract insightful features from the review text data. After text mining, the data is used to train different classification models using machine learning algorithms that detect fake reviews. After evaluating the models, a comparison is made based on the performance metrics. Furthermore, a web based user interface is created to provide a platform that combines the knowledge of the input user information with the chosen machine learning model to perform fake review detection on the input data.

General criteria of preventive healthcare based on the preventive care guidelines have been integrated with Electronic Health Record (EHR) systems through decision support systems and led to improved performance in healthcare delivery. Advanced integration which considers factors such as ethnicity, social history, medical history, family history need to be investigated. Integrating the preventive healthcare guidelines with the EHR based on above factors requires the extraction of the relevant information from these guidelines using text mining and natural language processing techniques. In this research, we propose a framework to extract information according to the EHR modules. Our results show that the proposed framework successfully extracts terms and concepts, and adequately maps them to the proposed data interchange structure that is based on the EHR functional modules. The extracted information and the populated data interchange structures eases the integration of the modifiable risk factors with the patient's records in the EHR. The proposed framework can be extended to other clinical healthcare guidelines where modifiable risk factors are critical.

Context: Following a trend already observed in the private sector, organizational image has become one of the concepts of greatest interest for public organizations, gaining strategic value. However, the topic is still in its early stages, and applicable methodological procedures show little diversity. Objective: The main objective of this research was to develop a tool, based on Artificial Intelligence techniques, capable of capturing the organizational image of public entities in the Triângulo Mineiro and Alto Paranaíba regions as constructed by the national press, focusing on news portals and newspapers. Method: This dissertation consists of two articles: the first adopts a qualitative approach, translated into a narrative literature review on the theme of public organizational image, considering the context of State Reform and modernization of the Brazilian Public Administration. The second article is theoretical-empirical, and through Text Mining and Natural Language Processing techniques, 1.570 news articles about the studied organization published between jan/2022 and sep/2023 were analyzed to diagnose the organizational image. Results: The first article resulted in a necessary update and consolidation of the organizational image concept, contributing to the understanding of the theme in a less- explored context. The results of the second article indicate the concentration of news about the studied organization in some press outlets; the existence of peaks in coverage; that the most frequently associated adjectives with the organization do not have a negative tone; and a list of main related topics. Research Relevance to the PPGADM Concentration Area and Research Line: For the development of this research, the case of the Municipal Department of Water and Sewage (DMAE) of the Municipality of Uberlândia was used to promote cooperation between academic, institutional, and social spheres for regional development. Impact and Innovative Character in Intellectual Production: This study promotes methodological renewal and innovation in data analysis techniques, going beyond traditional practices usually used in previous studies on the subject. Economic, Social, and Regional Impact: The results of this study bring contributions to practice translated into the development of management tools for public organizations. The developed tool produces inputs that allow organizations to guide communication actions and make decisions on a range of issues that contribute to the development of the organization, improvement of services, and increased citizen satisfaction. Regional Implications: It contributed to advancing the integration of Artificial Intelligence in the public sphere and the adoption of innovative tools; consequently, it can contribute to the leadership, strategy, and control implemented to assess, guide, and monitor the performance of regional public management, aiming at the implementation of public policies and the provision of services of interest to society. Sustainable Development Goals Addressed in the Research: This research aligns with SDG 16 – Peace, Justice, and Strong Institutions, contributing to Goals 16.6 and 16.7.

In recent years, there has been a mounting recognition of the influence that Social Determinants of Health (SDOH) exert on health outcomes, especially for chronic illnesses. Chronic illnesses such as congestive heart failure (CHF) are the leading cause of hospitalization for older adults in the United States. Understanding the web of associations between SDOH, socioeconomic factors that affect health, and CHF might help reduce the high incidences of morbidity, mortality, and cost associated with this condition. This paper describes a method for uncovering developing trends, patterns, topics, and previously unknown correlations between the SDOH and CHF using the hierarchical clustering method and Latent Dirichlet allocation topic modeling procedure on the statistical software R. These text mining and natural language processing techniques will be used to extract, analyze, and synthesize textual data from a variety of recent sources, such as medical literature (journals and conference proceedings), and reports from public health institutes to expose the influence of non-traditional risk factors on the onset, progression, and successful management of CHF. The major strength of the proposed method is that it employs an unsupervised machine learning technique to identify all themes and latent relationships from thousands of medical publications on SDOH and CHF, which would otherwise be time-consuming and labor-intensive, or even humanly impossible to uncover using the standard traditional method of literature review.

Understanding network reliability and outages is critical to the “health” of the Internet infrastructure. Unfortunately, our ability to analyze Internet outages has been hampered by the lack of access to public information from key players. In this paper, we leverage a somewhat unconventional dataset to analyze Internet reliability—the outages mailing list. The mailing list is an avenue for network operators to share information and insights about widespread outages. Using this unique dataset, we perform a first-of-its-kind longitudinal analysis of Internet outages from 2006 to 2013 using text mining and natural language processing techniques. We observe several interesting aspects of Internet outages: a large number of application and mobility issues that impact users, a rise in content, mobile issues, and discussion of large-scale DDoS attacks in recent years.

The Special Interest Group (SIG) on Text Mining (or BioLINK — Biological Literature, Information and Knowledge; http://www.pdg.cnb.uam.es/BioLINK/) was created to address the need for communication and interchange of ideas in the field of text mining and information extraction applied to biology and biomedicine. Information extraction (IE) is an outgrowth of work in automated natural language processing, which began in the 1950s with work on transformational grammar by Zellig Harris [5,6] and later Noam Chomsky [3,4]. Information extraction technology made rapid progress starting in the late 1980s, thanks to a series of conferences focused on evaluation of IE: the Message Understanding Conferences [1]. There is also a long history of research on applications in medicine. Applications to the medical field focus on two distinct sub-problems: improved access to the medical literature and extraction of information from patient records. Despite these successes in other fields, natural language processing (NLP) techniques were not introduced in biology until the late 1990s. Even today, there are two distinct groups: on the one hand, researchers with a background in computer science, and on the other hand, their colleagues with a background in the life sciences, with only limited interaction between the two groups. To improve this situation, the BioLINK group holds regular open meetings to bring together researchers developing text data mining tools and related language processing methods to manage the information explosion in the biomedical field. They include invited and contributed papers, with a focus on developing shared infrastructure (tools, corpora, ontologies) and challenge evaluations, in the style of the KDD Challenge Cups [2]. This year, the BioLINK SIG meeting focused on resources and tools for text mining, with special emphasis on the evaluation of these tools. Speakers from the following areas were invited:

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IntroductionSince the Coronavirus disease 2019 (COVID-19), public safety measures, including social distancing and school closures, have been implemented, precipitating psychological difficulties and heightened online activities for adolescents. However, studies examining the impact of the pandemic on adolescent mental health and their coping strategies in Asian countries are limited. Further, most studies have used survey measures to capture mental health challenges so far. Accordingly, this study aimed to examine the psychological challenges South Korean adolescents experienced and their coping strategies during the pandemic using the Natural Language Processing (NLP) and Text mining (TM) technique on adolescents’ social media texts/posts.MethodsThe data were gathered from social media texts/posts such as online communities, Twitter, and personal blogs from January 1, 2019, to October 31, 2021. The 12,520,250 texts containing keywords related to adolescents’ common psychological difficulties reported during the pandemic, including self-harm, Attention-Deficit/Hyperactivity Disorders (ADHD), and insomnia, were analyzed by TM, NLP using information extraction, co-occurrence and sentiment analysis. The monthly frequency of the keywords and their associated words was also analyzed to understand the time trend.ResultsAdolescents used the word “self-harm” in their social media texts more frequently during the second wave of COVID-19 (August to September 2020). “Friends” was the most associated word with “self-harm.” While the frequency of texts with “Insomnia” stayed constant throughout the pandemic, the word “ADHD” was increasingly mentioned in social media. ADHD and insomnia were most frequently associated with ADHD medications and sleeping pills, respectively. Friends were generally associated with positive words, while parents were associated with negative words.ConclusionDuring COVID-19, Korean adolescents often expressed their psychological challenges on social media platforms. However, their coping strategies seemed less efficient to help with their difficulties, warranting strategies to support them in the prolonged pandemic era. For example, Korean adolescents shared psychological challenges such as self-harm with friends rather than their parents. They considered using medicine (e.g., sleeping pills and ADHD medication) as coping strategies for sleep and attention problems.