Text Mining Algorithms Research Articles

Developing a Knowledge-Based Online Recommendation System to Guide STD Patients Through Their Journey

Sexually transmitted diseases (STDs) significantly impact public health, affecting one in five U.S. adults and imposing substantial economic burdens. Many at-risk individuals seek information and support online rather than through regular testing, facing challenges due to fragmented information and a lack of personalized recommendations. This study develops an online health recommendation system (OHRS) tailored for STD patients, integrating informational and emotional support based on their disease journey. Using a BERT-based named entity recognition (NER) algorithm, the system identifies patient emotions and stages from online posts, providing relevant support resources. Data collection for designing a recommendation engine included analyzing online posts and consulting healthcare experts. The system's effectiveness was validated through user-based simulation studies. Key contributions include developing text-mining algorithms, creating a knowledge-based recommendation system, and proposing design principles for AI-driven support systems for stigmatized conditions.

The incidence and prevalence of Dupuytren’s disease in primary care: results from a text mining approach on registration data

Background The focus of research and management of Dupuytren’s disease (DD) is shifting from relieving symptoms in the later stages of disease towards the prevention of contractures. Treatment services might likewise shift towards primary care. Studying characteristics of DD patients who seek medical care for the first time, may identify a symptomatic target group for early DD treatments. We present the first study that estimates the incidence and prevalence of DD in primary care by applying a text-mining algorithm to registration data. Methods This is a population-based cohort study using electronic health records from Dutch general practices involved in a regional research network. Descriptive statistics were used to describe sex, age, comorbidities and lifestyle factors, the latter two were identified via International Classification of Primary Care (ICPC) codes. Incidence rate was calculated as number of patients with a first contact for DD/1000 person years for the years 2017–2021, point prevalence as the percentage of patients with a contact for DD in 2021. DD contacts were identified using a text-mining algorithm. Results The incidence ranged between 1.41 and 1.72/1000 person years and the overall prevalence was 1.99%. Incidence and prevalence are higher among males and increase with age, peaking between 61 and 80 years. Conclusions Our results of prevalence and incidence of DD in primary care give an insight into the relevant population of patients with symptomatic DD that might be the future target group for potential disease controlling treatments.

Development and Portability of a Text Mining Algorithm for Capturing Disease Progression in Electronic Health Records of Patients With Stage IV Non-Small Cell Lung Cancer.

The objective was to develop and evaluate the portability of a text mining algorithm for prospectively capturing disease progression in electronic health record (EHR) data of patients with metastatic non-small cell lung cancer (mNSCLC) treated with immunochemotherapy. This study used EHR data from patients with mNSCLC receiving immunochemotherapy (between October 1, 2018, and December 31, 2022) in four Dutch hospitals. A text mining algorithm for capturing disease progression was developed in hospitals 1 and 2 and then transferred to hospitals 3 and 4 to evaluate portability. Performance metrics were calculated by comparing its outcomes with manual chart review. In addition, data were simulated to come available over time to assess performance in real-time applications. Median progression-free survival (PFS) was calculated using the Kaplan-Meier method to compare text mining with manual chart review. During development and portability, the text mining algorithm performed well in capturing disease progression, with all performance scores >90%. When real-time performance was simulated, the performance scores in all four hospitals exceeded 90% from week 15 after the start of follow-up. Although the exact progression dates varied in 46 patients of 157 patients with progressive disease, the number of patients labeled with progression too early (n = 24) and too late (n = 22) was well balanced with discrepancies ranging from -116 to 384 days. Nevertheless, the PFS curves constructed with text mining and manual chart review were highly similar for each hospital. In this study, an accurate text mining algorithm for capturing disease progression in the EHR data of patients with mNSCLC was developed. The algorithm was portable across different hospitals, and the performance over time was good, making this an interesting approach for prospective follow-up of multicenter cohorts.

Antibiotic administration for open reduction internal fixation of closed ankle fractures: Is one preoperative dose enough?

Although preoperative antibiotics are used routinely in ankle fracture surgery, there is considerable variability in postoperative antibiotic dosing regimens amongst surgeons. The aim of this study is to compare the rate of surgical site infection (SSI) in patients receiving one dose of preoperative antibiotics to patients who received more than one dose of antibiotics. A single-center Level I Trauma Center retrospective review of patients with isolated closed ankle fractures who underwent open reduction internal fixation over a 10-year period was performed. Demographics data and risk factors were obtained from the electronic medical record. SSI was detected using a text-mining algorithm on all postoperative clinic notes. Factors associated with the development of SSI or increased antibiotic dosing were evaluated. Eight-hundred and twenty-six patients were included in the analysis. There was no correlation between infection rate and any of the potential risk factors evaluated. Two-hundred and ninety-two patients received only one dose of antibiotics preoperatively and 534 patients received more than one antibiotic dose. The rate of SSI was not significantly different between the single-dose cohort (5.8 %) and the multi-dose cohort (3.9 %) (p=0.215). Routine use of postoperative antibiotics in open reduction internal fixation of closed ankle fractures did not decrease the rate of SSI, regardless of associated comorbidities. Level of evidence: Level III (therapeutic retrospective cohort study)

The biomedical relationship corpus of the BioRED track at the BioCreative VIII challenge and workshop.

The automatic recognition of biomedical relationships is an important step in the semantic understanding of the information contained in the unstructured text of the published literature. The BioRED track at BioCreative VIII aimed to foster the development of such methods by providing the participants the BioRED-BC8 corpus, a collection of 1000 PubMed documents manually curated for diseases, gene/proteins, chemicals, cell lines, gene variants, and species, as well as pairwise relationships between them which are disease-gene, chemical-gene, disease-variant, gene-gene, chemical-disease, chemical-chemical, chemical-variant, and variant-variant. Furthermore, relationships are categorized into the following semantic categories: positive correlation, negative correlation, binding, conversion, drug interaction, comparison, cotreatment, and association. Unlike most of the previous publicly available corpora, all relationships are expressed at the document level as opposed to the sentence level, and as such, the entities are normalized to the corresponding concept identifiers of the standardized vocabularies, namely, diseases and chemicals are normalized to MeSH, genes (and proteins) to National Center for Biotechnology Information (NCBI) Gene, species to NCBI Taxonomy, cell lines to Cellosaurus, and gene/protein variants to Single Nucleotide Polymorphism Database. Finally, each annotated relationship is categorized as 'novel' depending on whether it is a novel finding or experimental verification in the publication it is expressed in. This distinction helps differentiate novel findings from other relationships in the same text that provides known facts and/or background knowledge. The BioRED-BC8 corpus uses the previous BioRED corpus of 600 PubMed articles as the training dataset and includes a set of newly published 400 articles to serve as the test data for the challenge. All test articles were manually annotated for the BioCreative VIII challenge by expert biocurators at the National Library of Medicine, using the original annotation guidelines, where each article is doubly annotated in a three-round annotation process until full agreement is reached between all curators. This manuscript details the characteristics of the BioRED-BC8 corpus as a critical resource for biomedical named entity recognition and relation extraction. Using this new resource, we have demonstrated advancements in biomedical text-mining algorithm development. Database URL: https://codalab.lisn.upsaclay.fr/competitions/16381.

P-773 The absence and heterogeneity of pregnancy outcome definitions in randomized controlled trials of infertility: an overview of the literature

Abstract Study question What proportion of infertility trials reported pregnancy and live birth, how often were these outcomes defined, and what is the consistency in their definitions? Summary answer Almost 2/3 of infertility trials did not report live birth. One in four did not define clinical pregnancy. There was considerable heterogeneity in outcome definitions. What is known already The absent or inconsistent outcome definitions in randomized controlled trials (RCTs) complicate the interpretation of the trial results and undermine evidence synthesis. While reporting live births in infertility trials has been a long-running recommendation, the extent to which this is adhered to is unclear. Furthermore, it is unknown if the use of core outcome sets in infertility has improved the standardization of outcome reporting and what is the uptake of its recommendation on reporting live birth. Study design, size, duration We studied all RCTs in reproductive medicine published between 2012 and 2023. We aim to ascertain i) whether clinical pregnancy, ongoing pregnancy and live birth were reported; and ii) whether and how these pregnancy outcomes were defined. Participants/materials, setting, methods We systematically searched Embase, Medline and CENTRAL for RCTs in reproductive medicine from 2012 to 2023. RCTs involving infertile women that reported either biochemical pregnancy, clinical pregnancy, ongoing pregnancy, or live birth were eligible, while conference abstracts, secondary analyses or non-English written RCTs were not. Two authors independently screened articles. We used text-mining to extract pregnancy definitions in R. The accuracy of automatic data extraction was verified with a random sample of 20 articles. Main results and the role of chance After screening 5708 articles, 1,311 were included, of which 21 have been retracted. Manual checks of 20 papers confirmed the accuracy of text-mining. Among 1311 RCTs, 61% of articles reported pregnancy without data on live birth, while only 14% of all RCTs reported on clinical pregnancy, ongoing pregnancy and live birth; 13% of RCTs reported pregnancy without specifying a definition (biochemical, clinical or ongoing). Definitions for clinical pregnancy, ongoing pregnancy and live birth were not provided in 27% (270/1010), 39% (153/389) and 68% (373/549) of articles reporting data on respectively clinical pregnancy, ongoing pregnancy and live birth. Among 740 clinical pregnancy definitions, 46% included gestational age (weeks), ranging from 3 to 16 weeks, typically at 6 to 7 weeks in 40% of definitions. Gestational sac and fetal heartbeat appeared respectively in 64% and 55% of the definitions. Of 236 ongoing pregnancy definitions, 94% included gestational age (weeks), stretching from 6 to 28 weeks, typically at 12 weeks in 52% of definitions. Gestational sac and fetal heartbeat appeared in 9% and 63% of the definitions, respectively. Among 176 live birth definitions, 64% used a gestational age (weeks) threshold, varying from 20 to 34 weeks, typically at week 24 in 30% of definitions. Limitations, reasons for caution While we optimized the text-mining algorithm attempting to identify all types of outcome definitions and manually curated all extracted definitions, there were definitions that we incorrectly omitted. Wider implications of the findings Despite recommendations to report live births in core outcome sets, only a third of RCTs did so, highlighting a disconnection between the advocated outcome and what researchers are reporting. The significant lack and variability of pregnancy definitions underscore the imperative to increase the dissemination and uptake of standardized pregnancy outcomes. Trial registration number Not applicable

Development of a text mining algorithm for identifying adverse drug reactions in electronic health records.

Adverse drug reactions (ADRs) are a significant healthcare concern. They are often documented as free text in electronic health records (EHRs), making them challenging to use in clinical decision support systems (CDSS). The study aimed to develop a text mining algorithm to identify ADRs in free text of Dutch EHRs. In Phase I, our previously developed CDSS algorithm was recoded and improved upon with the same relatively large dataset of 35000 notes (Step A), using R to identify possible ADRs with Medical Dictionary for Regulatory Activities (MedDRA) terms and the related Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) (Step B). In Phase II, 6 existing text-mining R-scripts were used to detect and present unique ADRs, and positive predictive value (PPV) and sensitivity were observed. In Phase IA, the recoded algorithm performed better than the previously developed CDSS algorithm, resulting in a PPV of 13% and a sensitivity of 93%. For The sensitivity for serious ADRs was 95%. The algorithm identified 58 additional possible ADRs. In Phase IB, the algorithm achieved a PPV of 10%, a sensitivity of 86%, and an F-measure of 0.18. In Phase II, four R-scripts enhanced the sensitivity and PPV of the algorithm, resulting in a PPV of 70%, a sensitivity of 73%, an F-measure of 0.71, and a 63% sensitivity for serious ADRs. The recoded Dutch algorithm effectively identifies ADRs from free-text Dutch EHRs using R-scripts and MedDRA/SNOMED-CT. The study details its limitations, highlighting the algorithm's potential and significant improvements.

The Applications of Artificial Intelligence (AI)-Driven Tools in Virus-Like Particles (VLPs) Research.

Viral-like particles (VLPs) represent versatile nanoscale structures mimicking the morphology and antigenic characteristics of viruses, devoid of genetic material, making them promising candidates for various biomedical applications. The integration of artificial intelligence (AI) into VLP research has catalyzed significant advancements in understanding, production, and therapeutic applications of these nanostructures. This comprehensive review explores the collaborative utilization of AI tools, computational methodologies, and state-of-the-art technologies within the VLP domain. AI's involvement in bioinformatics facilitates sequencing and structure prediction, unraveling genetic intricacies and three-dimensional configurations of VLPs. Furthermore, AI-enabled drug discovery enables virtual screening, demonstrating promise in identifying compounds to inhibit VLP activity. In VLP production, AI optimizes processes by providing strategies for culture conditions, nutrient concentrations, and growth kinetics. AI's utilization in image analysis and electron microscopy expedites VLP recognition and quantification. Moreover, network analysis of protein-protein interactions through AI tools offers an understanding of VLP interactions. The integration of multi-omics data via AI analytics provides a comprehensive view of VLP behavior. Predictive modeling utilizing machine learning algorithms aids in forecasting VLP stability, guiding optimization efforts. Literature mining facilitated by text mining algorithms assists in summarizing information from the VLP knowledge corpus. Additionally, AI's role in laboratory automation enhances experimental efficiency. Addressing data security concerns, AI ensures the protection of sensitive information in the digital era of VLP research. This review serves as a roadmap, providing insights into AI's current and future applications in VLP research, thereby guiding innovative directions in medicine and beyond.

Primary care for cancer survivors: a review of national institutes of health-funded grants 2017-2022.

To describe the characteristics of National Institutes of Health (NIH) grants on primary care cancer research in cancer survivorship funded over the past 5years. Research project grants (RPG) funded during Fiscal Year (FY) 2017 to 2022 focused on cancer survivorship were identified using a text mining algorithm of words from the NIH Research, Condition, and Disease Categorization (RCDC) thesaurus with survivorship-relevant terms. Grants were then reviewed and double-coded to identify those that were carried out in a primary care setting, targeted primary care providers, or had primary care providers in the study team. A total of 24 grants were identified; 23 were funded by the National Cancer Institute and one was funded by the National Institute on Minority Health and Health Disparities. The majority were funded under the R01 mechanism (70.8%) and led by established investigators. Most were interventional design (91.7%), including both survivors and providers (79.2%), and focused care coordination or healthcare utilization (91.7%). Grants focused on primary care cancer survivorship are uncommon in the NIH portfolio. For the over 18 million cancer survivors in the USA, being cared for in a primary care setting is common. Yet, NIH-funded research on primary care cancer survivorship is sparse.

Analyzing group polarization through text emotion measurement and time series prediction: A comparative study across three online platforms

This study investigated the emotional trends of users on social platforms, considering the event of “the Changsha girl jumping off the Lalamove truck” as a case study. It examined the effects of recommendation algorithms and group social comparison attributes on group emotions across three platforms: Zhihu, Weibo, and Bilibili. Through text mining and emotion analysis algorithms, group reviews were analyzed, and an event-based ARIMA robustness detection model was constructed using time series data. Utilizing the theoretical framework of the social comparison process, the study discovered that the “information database” formed by the recommendation algorithms of social platforms fosters the emergence of emotional group polarization among users. Furthermore, the findings revealed that the audience's social comparison attributes play a role in shaping emotional group polarization. High knowledge attributes tend to inhibit emotional group polarization, while low knowledge attributes tend to promote it. Machine learning algorithms were employed to measure user sentiment in social media platform comments, revealing insights into the causes of group polarization through a comparison of social comparison attributes and algorithm techniques. Future studies must focus on measuring technical information entropy.

Sentiment analysis applied to tourism: exploring tourist-generated content in the case of a wellness tourism destination

ABSTRACT Sentiment analysis of content generated by tourists through social media platforms can be used to generate knowledge about a wellness tourism destination. The present research aims to explore the content generated by tourist reviews, analyse the sentiments about a wellness tourism destination made in comments on hotels, evaluate the sentiment of tourists towards the various components of this kind of destination, and identify the motivations for tourists to look for wellness tourism destinations, such as the Algarve. The analysis of the reviews of the tourism components demonstrates tourists’ opinions about the wellness destination and also grades the infrastructure facilities and the highlighted services for the categories of this type of tourism. In attaining the objectives of this study, quantitative methods were used to review 1294 comments extracted from TripAdvisor, to which text mining algorithms were applied to assess the sentiment expressed in the comments. The results show that the three keywords most frequently used for all component categories in the Algarve wellness destinations are “great”, “love” and “visit”, and also reveal that a hotel providing spa and massage services in the tourist wellness destination mainly satisfies the desires of a tourist.

Text Mining and Financial Analysis Modeling for Financial Statement Disclosure

This paper explores the integration of financial analysis, text mining, and blockchain technology to enhance financial statement disclosure and analysis processes. Leveraging text mining algorithms, the study extracts valuable insights from unstructured financial data, unveiling hidden patterns and sentiments within financial statements. Additionally, blockchain technology is employed to ensure secure and immutable storage of financial data, fostering transparency and trust in financial transactions and disclosures. Through the automation and digitization of financial reporting processes, stakeholders can streamline financial statement analysis, identify risks and opportunities, and make data-driven decisions with greater accuracy and efficiency.

Assessment of transparency indicators in space medicine.

Space medicine is a vital discipline with often time-intensive and costly projects and constrained opportunities for studying various elements such as space missions, astronauts, and simulated environments. Moreover, private interests gain increasing influence in this discipline. In scientific disciplines with these features, transparent and rigorous methods are essential. Here, we undertook an evaluation of transparency indicators in publications within the field of space medicine. A meta-epidemiological assessment of PubMed Central Open Access (PMC OA) eligible articles within the field of space medicine was performed for prevalence of code sharing, data sharing, pre-registration, conflicts of interest, and funding. Text mining was performed with the rtransparent text mining algorithms with manual validation of 200 random articles to obtain corrected estimates. Across 1215 included articles, 39 (3%) shared code, 258 (21%) shared data, 10 (1%) were registered, 110 (90%) contained a conflict-of-interest statement, and 1141 (93%) included a funding statement. After manual validation, the corrected estimates for code sharing, data sharing, and registration were 5%, 27%, and 1%, respectively. Data sharing was 32% when limited to original articles and highest in space/parabolic flights (46%). Overall, across space medicine we observed modest rates of data sharing, rare sharing of code and almost non-existent protocol registration. Enhancing transparency in space medicine research is imperative for safeguarding its scientific rigor and reproducibility.

From bibliometrics to text mining: exploring feature selection methods in microarray research

Text mining (TM) is a technique that aims to extract knowledge from unstructured data sources by transforming them into structured data. TM algorithms can be used to detect hidden patterns in large amounts of data, including bibliometric data. Feature selection has been used to reduce the high dimensionality and complexity of computational problems, including microarray data that have a large number of features. In this context, this study aims to use text mining to discover trends in the use of feature selection techniques on microarray data based on bibliometric data such as titles, abstracts, and keywords. A total of 1448 studies published in journals indexed in the Web of Science database were collected to perform a bibliometric and TM analysis. One of the main goals of this study was to determine the patterns related to the roles of medical and machine learning methods. The results demonstrated the trends between microarray and other medical/biological topics, and machine learning techniques such as feature selection and classification, including the identification of commonly used databases and algorithms. Colon, lung, and breast were the most commonly studied cancers identified using microarray data and feature selection techniques. In addition, SVM was frequently used for dimensionality reduction and classification tasks. Despite the insightful results based on text mining, more studies are needed to investigate the performance, strength, and weakness of different types of feature selectors to microarray data.

Sustainable Stewardship of Egypt’s Iconic Heritage Sites: Balancing Heritage Preservation, Visitors’ Well-Being, and Environmental Responsibility

While sustainability is widely recognized as necessary for managing heritage sites, balancing preservation demands with tourism development pressures presents significant challenges. A critical gap in the existing research relates to understanding the recurring issues that undermine the long-term sustainability of prominent archaeological destinations in Egypt and visitor well-being. Specifically, more research is needed to identify the specific sustainability and visitor experience problems that persist across Egypt’s major archaeological sites despite ongoing management efforts. Addressing this knowledge gap could help inform strategies for these destinations to balance conservation priorities with tourism in a more sustainable manner that enhances visitors’ cultural exploration. This study contributes a novel multisite content analysis of over 4423 online reviews from TripAdvisor pertaining to three of Egypt’s premier archaeological destinations—Giza Pyramids, Al Mu’izz Street, and Saqqara. Using established text mining methodologies and algorithms within Voyant Tools, critical challenges were identified through word frequency, concept network, and trend analyses. Recurring issues documented included uncontrolled commercialization undermining the ambiance of historic locales, environmental degradation from inadequate waste management and tourism overflows, and overcrowding compromising visitor comfort, health and safety. Deficiencies in signage, transportation, and other tourist services were also prominent themes. The holistic evaluation of cross-cutting concerns faced at the diverse yet interrelated sites advanced the theoretical knowledge on cooperative governance models and competencies necessary for integrative heritage management. The recommendations center on collaborative stakeholder partnerships to reform commercial practices through licensing and enforcement, upgrading aging infrastructure to support sustainable tourism volumes, and implementing zoning and carrying capacity policies tailored to local community contexts. The proposed solutions aim to safeguard Egypt’s irreplaceable cultural treasures by addressing recurrent problems that undermine environmental, economic and socio-cultural sustainability if left unresolved, positioning its archaeological wonders for appreciation and study by present and future generations.

Uncovering the dark side of the sharing economy from a provider’s perspective: a bibliometric systematic review

PurposeDespite a growing body of research focusing on the dark side of sharing economy development, arguments are fragmented and incomplete. This study aims to address the gap by integrating existing viewpoints based on a provider’s perspective.Design/methodology/approachThis study conducted a bibliometric analysis using text mining and clustering algorithm techniques to measure the scope of scientific output on this topic and identify the main research themes.FindingsThrough the bibliometric analysis, this study developed an integrative framework based on the platform providers’ internal management issues and external conflicts with consumers, society, government regulations and traditional business. It also identified significant gaps within each research theme and proposed a future research agenda.Originality/valueSharing economy development has not yet been fully understood and regulated, leading to unprecedented challenges to existing business systems. The study addresses knowledge gaps and advances the understanding of the dark side of the sharing economy based on the provider’s internal management and interplay with external forces. It offers a roadmap for future research to advance understanding of the “hidden” dark side of the sharing economy.

The communication reaction function of the European Central Bank. An analysis using topic modelling

ABSTRACT Central bank communication plays a crucial role in the conduct of monetary policy, yet the research on central bank communication, while growing, is still scarce. In this paper, we analyze the communication reaction function of the European Central Bank (ECB) through topic-based indices derived from the bank’s speeches. These indices are used as dependent variables in policy and communication reaction function models, as suggested by recent literature. The topics are extracted using Latent Dirichlet Allocation (LDA), a popular text mining algorithm for topic extraction. The ECB has recently reviewed its monetary policy strategy, which led to an increase in studies incorporating the new methods offered by text mining for analyzing the policy reaction function of the bank. We show how indices built through topic modelling can be used to study the communication reaction function of a central bank, and we examine which variables are significant for every topic communicated by the ECB.

Digital Transformation Strategy for Financial Management of Entity Enterprises in the Information Age

Abstract This study is dedicated to exploring the effectiveness of digital transformation of financial management of real enterprises in the information age. The annual reports of enterprises are collected from 2011 to 2022 by web crawler technology, and text mining algorithms, such as TF-IDF and LDA theme model, are used to establish a measurement index system of digital transformation of financial management, which is calculated by entropy weight-TOPSIS method. The results show that keywords related to digital transformation, such as “informatization” and “intelligence”, appear frequently, and the importance of the theme changes significantly in different transformation stages. During the rapid transformation period, the frequency of words such as “industrial internet” and “smart grid” exceeds 400 times. Some companies have seen significant improvements in debt servicing, operations and profitability. Digital transformation of financial management is efficacious in improving operational efficiency and profitability. Still, it is a dynamic process that requires enterprises to choose appropriate strategies based on their characteristics. The study emphasizes that enterprises should pay close attention to integrating and applying emerging digital technologies and optimize their internal governance structure to promote sustainable performance improvement.

Artificial Intelligence Empowers Emotional Expression and Aesthetic Imagery in Modern Chinese Literature

Abstract This paper explores how artificial Intelligence enhances emotional expression and aesthetic imagery in modern Chinese literature. The study focuses on the latent semantic analysis of literary texts, text mining challenges, techniques and algorithms for latent semantic analysis, and the application of artificial Intelligence in literary criticism. Through text representation, analysis of text mining challenges, latent semantic analysis (LSA), and the application of specific algorithms, this study provides an in-depth understanding of the intrinsic semantics of literary texts. The study employs text representation methods, such as the TF-IDF formulation, to deal with high-dimensional sparse text data. The main challenges include high dimensionality and discovering potential semantic relationships between words. Latent semantic analysis (LSA) realizes data dimensionality reduction through SVD singular value decomposition technique to extract the implicit relationships between words in text. The results show that LSA effectively pulls the potential semantic structure of the text, reduces the “noise”, simplifies the text vectors, and realizes the dimensionality reduction. In addition, the text clustering analysis using Kohonen self-organized feature mapping network reveals the semantic relationship and sentiment distribution among literary works. Intelligent technology can effectively enhance literary works’ emotional depth and aesthetic value and provide a new interpretation perspective for modern literature.

Childhood Experiences and Needs: Parental Goals, Mistakes, and Personality Traits and Their Adult Daughters’ Ability to Meet Their Needs

Objectives of the research:. The purpose of this study was to determine whether women’s childhood experiences are related to their ability to meet their needs in adulthood.
 Research methods: The survey was conducted online with a random sample of 402 women between the ages of 21 and 50. In order to answer the research questions, text mining algorithms and cluster analysis carried out by data mining algorithms were used.
 A short description of the context of the issue: The ability to fulfill one’s needs is an important determinant of personal happiness, well-being and mental health The study examined whether women who had experienced fewer parental mistakes were better at meeting their needs, and whether there were intergroup differences between women who were more and less efficient at fulfilling their needs in terms of the personality traits their parents tried to mold in them during childhood and the personality traits that the women developed.
 Research findings: The results revealed that women who experienced fewer parenting errors in childhood and whose parents tried to shape in them personality traits such as Stability (Alpha-Plus), Plasticity (Beta-Plus) and Integration (Gamma-Plus) indeed developed these traits and were more effective in fulfilling their needs in adulthood.
 Conclusions and recommendations: Experiencing more parenting mistakes in childhood is associated with the development of unfavorable personality traits and lower competence in meeting needs in adulthood.