Text Mining Studies Research Articles

Analysis of Metaverse and Cryptocurrency Crimes ın Forensic Accounting

The purpose of this study is to collect data on crimes that encompass the technological and software aspects referred to as the "new generation," and to analyze case studies and legal incidents related to crimes committed within the scope of the metaverse and cryptocurrency.
 The starting point of the study is the analysis of court decisions related to metaverse and cryptocurrency crimes. Within the scope of the study, thousands of court decisions have been scanned using text mining techniques. The keywords "metaverse," "bitcoin," "digital currency," "cryptocurrency," and "digital money" have been included in the search algorithm for the word corpus. Although no court decision containing the word "metaverse" was found, 37 decisions were identified in which the other terms from the word corpus were mentioned. The oldest decision dates back to July 12, 2019, while the most recent decision is dated March 23, 2023. In addition to the decisions of first-instance courts, the decisions of appellate and higher judicial authorities have also been examined within the framework of text mining studies.

Text Mining Analysis of Patent in Innovation Studies: Trends, Issues and Future Research Agenda

Innovation is a cornerstone of progress, and patents play a pivotal role in protecting and promoting novel ideas and technologies. This paper delves into the dynamic intersection of text mining and innovation studies by focusing on patent data. This literature review paper presents an in-depth assessment of the trends and developments in text mining techniques as they relate to patent analysis within the context of innovation studies. This paper presents a thorough study of text mining literature through a systematic literature review of 162 articles in 20 peer-reviewed journals published from 2003 to 2022. We undertake a thorough literature review in this study, identifies themes and categorize the papers into five clusters of Technology opportunities identification, Firm competition strategies, Emerging technology forecasting and evaluation, Patent technical intelligence, Technological convergence and open-endedness. The study shows that most articles concentrated on technological firms and focused on technological-driven strategies. This paper highlights key results, open issues and outlines a compelling research agenda for further investigation and development in the field of text mining in innovation studies related to patents

An Enhanced and Adaptive Proximity Measure Algorithm for the Screening of Fake and Clone Social-Media Profiles

Abstract: The term "semantics" has been brought up in a broad range of text mining studies due to the significant role that text semantics plays in establishing the meaning of a document. On the other hand, there is a dearth of research that synthesises the several subfields of research and offers a synopsis of the works that have been produced. This paper gives an in-depth mapping of research that focuses on semantics and text mining. In order to produce an accurate map, the researchers followed a regimented set of protocols throughout their work. The results were gleaned from a total of 1693 pieces of study that were selected from a pool of 3984 publications that were housed in five different online libraries. Academics who are working on semantics-focused text mining may find the mapping that was generated to be useful as a reference. The mapping gives a fundamental overview of the subject matter, pinpoints certain places that need the construction of primary or secondary studies, and identifies particular regions in need of the creation of either. It shows that the processing of semantic components in text mining is still an open research issue even though various studies have been developed despite the fact that these studies have been established.

A Genome-Wide Functional Screen Identifies Enhancer and Protective Genes for Amyloid Beta-Peptide Toxicity.

Alzheimer's disease (AD) is known to be caused by amyloid β-peptide (Aβ) misfolded into β-sheets, but this knowledge has not yet led to treatments to prevent AD. To identify novel molecular players in Aβ toxicity, we carried out a genome-wide screen in Saccharomyces cerevisiae, using a library of 5154 gene knock-out strains expressing Aβ1-42. We identified 81 mammalian orthologue genes that enhance Aβ1-42 toxicity, while 157 were protective. Next, we performed interactome and text-mining studies to increase the number of genes and to identify the main cellular functions affected by Aβ oligomers (oAβ). We found that the most affected cellular functions were calcium regulation, protein translation and mitochondrial activity. We focused on SURF4, a protein that regulates the store-operated calcium channel (SOCE). An in vitro analysis using human neuroblastoma cells showed that SURF4 silencing induced higher intracellular calcium levels, while its overexpression decreased calcium entry. Furthermore, SURF4 silencing produced a significant reduction in cell death when cells were challenged with oAβ1-42, whereas SURF4 overexpression induced Aβ1-42 cytotoxicity. In summary, we identified new enhancer and protective activities for Aβ toxicity and showed that SURF4 contributes to oAβ1-42 neurotoxicity by decreasing SOCE activity.

Integrated Approaches to Identify miRNA Biomarkers Associated with Cognitive Dysfunction in Multiple Sclerosis Using Text Mining, Gene Expression, Pathways, and GWAS.

Multiple sclerosis (MS), a chronic autoimmune disorder, affects the central nervous system of many young adults. More than half of MS patients develop cognition problems. Although several genomic and transcriptomic studies are currently reported in MS cognitive impairment, a comprehensive repository dealing with all the experimental data is still underdeveloped. In this study, we combined text mining, gene regulation, pathway analysis, and genome-wide association studies (GWAS) to identify miRNA biomarkers to explore the cognitive dysfunction in MS, and to understand the genomic etiology of the disease. We first identified the dysregulated miRNAs associated with MS and cognitive dysfunction using PubTator (text mining), HMDD (experimental associations), miR2Disease, and PhenomiR database (differentially expressed miRNAs). Our results suggest that miRNAs such as hsa-mir-148b-3p, hsa-mir-7b-5p, and hsa-mir-7a-5p are commonly associated with MS and cognitive dysfunction. Next, we retrieved GWAS signals from GWAS Catalog, and analyzed the enrichment analysis of association signals in genes/miRNAs and their association networks. Then, we identified susceptible genetic loci, rs17119 (chromosome 6; p = 1 × 10−10), rs1843938 (chromosome 7; p = 1 × 10−10), and rs11637611 (chromosome 15; p = 1.00 × 10−15), associated with significant genetic risk. Lastly, we conducted a pathway analysis for the susceptible genetic variants and identified novel risk pathways. The ECM receptor signaling pathway (p = 3.98 × 10−8) and PI3K/Akt signaling pathway (p = 5.98 × 10−5) were found to be associated with differentially expressed miRNA biomarkers.

DISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labels

Nowadays, there are many applications of text mining over corpora from different languages. However, most of them are based on texts in prose, lacking applications that work with poetry texts. An example of an application of text mining in poetry is the usage of features derived from their individual words in order to capture the lexical, sublexical and interlexical meaning, and infer the General Affective Meaning (GAM) of the text. However, even though this proposal has been proved as useful for poetry in some languages, there is a lack of studies for both Spanish poetry and for highly-structured poetic compositions such as sonnets. This article presents a study over an annotated corpus of Spanish sonnets, in order to analyse if it is possible to build features from their individual words for predicting their GAM. The purpose of this is to model sonnets at an affective level. The article also analyses the relationship between the GAM of the sonnets and the content itself. For this, we consider the content from a psychological perspective, identifying with tags when a sonnet is related to a specific term. Then, we study how GAM changes according to each of those psychological terms. The corpus used contains 274 Spanish sonnets from authors of different centuries, from fifteenth to nineteenth. This corpus was annotated by different domain experts. The experts annotated the poems with affective and lexico-semantic features, as well as with domain concepts that belong to psychology. Thanks to this, the corpus of sonnets can be used in different applications, such as poetry recommender systems, personality text mining studies of the authors, or the usage of poetry for therapeutic purposes.

Discovering the same job ads expressed with the different sentences by using hybrid clustering algorithms

Text mining studies on job ads have become widespread in recent years to determine the qualifications required for each position. It can be said that the researches made for Turkish are limited while a large resource pool is encountered for the English language. Kariyer.Net is the biggest company for the job ads in Turkey and 99% of the ads are Turkish. Therefore, there is a necessity to develop novel Natural Language Processing (NLP) models in Turkish for analysis of this big database. In this study, the job ads of Kariyer.Net have been analyzed, and by using a hybrid clustering algorithm, the hidden associations in this dataset as the big data have been discovered. Firstly, all ads in the form of HTML codes have been transformed into regular sentences by the means of extracting HTML codes to inner texts. Then, these inner texts containing the core ads have been converted into the sub ads by traditional methods. After these NLP steps, hybrid clustering algorithms have been used and the same ads expressed with the different sentences could be managed to be detected. For the analysis, 57 positions about Information Technology sectors with 6,897 ad texts have been focused on. As a result, it can be claimed that the clusters obtained contain useful outcomes and the model proposed can be used to discover common and unique ads for each position.

Research trends in text mining: Semantic network and main path analysis of selected journals

In this study, network and main path analyses were conducted on 1856 studies related to text mining, by extracting keywords and citation information from the text of each paper. Our findings indicate that research papers on text mining have been published in 45 academic disciplines in the 1980s and 1990s, 105 disciplines in the 2000s, and 171 disciplines in the 2010s. The results show that using text mining as a research topic and method has rapidly increased. We also demonstrate that the main theme of text mining research is discourse and content analysis in the 1980s and 1990s, biology and data mining in the 2000s, and medicine and advanced text mining in the 2010s. Moreover, we examined the main citation path for text mining studies and suggest that the main focus of text mining studies has evolved from information science to information systems and technology management. Additionally, influential papers have been recently published in fields such as architecture and social ecology revealing the wide scope of text mining. This article presents an understanding of previously unexplored research trends in text mining and how these trends shed light on the most influential academic papers in the field.

Identifying the Novel Pyrimidine-Based CDK2 Inhibitors as Anticancer Agents Using Text-Mining and Combined Molecular Modeling Approaches

The cycline-dependent kinase (CDK) protein is an important target used in anti-cancer drug designing studies. Pyrimidine is a crucial fragment which is used in many FDA-approved drugs including anti-cancer drugs. In the current study, a small molecule database (Specs SC) that includes more than 210.000 compounds were used in text mining studies and 6668 molecules that carry “pyrimidine” fragments were filtered. These compounds were then screened at the binding pocket of CDK-2 target using molecular docking and molecular dynamics (MD) simulations approaches. Binding free energies of screened compounds were also compared with pyrimidine-based FDA approved anti-cancer drug Abemaciclib which targets CDK. Based on comparison of docking scores of screened compounds, top-7 hits were used in 100 ns MD simulations. Same MD simulations protocol (100 ns) were also applied to Abemaciclib-bound CDK-2 complex structure. Average Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) energies were then calculated. Our MM/GBSA results showed that 6 out of 7 compounds have better MM/GBSA scores than FDA approved compound Abemaciclib. Thus, together with combination of text mining and integrated molecular modeling approaches, we identified novel pyrimidine-based hits against CDK.

The quality movement's three operational paradigms: a text mining venture

PurposeThis study aims to analyze four text-mining studies of quality management (QM) to illustrate and problematize how the research on quality has informed the quality paradigm since the 1980s. By understanding history, one can better manage current developments.Design/methodology/approachThe findings are based on a meta-analysis of four text-mining studies that explore and describe 11,579 research entries on quality between 1980 and 2017.FindingsThe findings show that the research on quality during the past 30 years form a research paradigm consisting of three operational paradigms: an operative paradigm of backend quality orbiting around QM, total QM (TQM) and service quality; an operative paradigm of middle-way quality, circling around the International Organization for Standardization (ISO), business excellence frameworks (BEFs) and quality awards; and an operative paradigm of frontend quality, revolving around reliability, costs and processes. The operative paradigms are interconnected and complementary; they also show a divide between a general management view of quality and a hands-on engineering view of quality. The findings indicate that the research on quality is a long-lived standalone paradigm, supporting the notion of quality being a genuine academic entity, not a fashion or fad.Research limitations/implicationsThe empirical basis of the study is four text-mining studies. Consequently, the results and findings are based on a limited number of findings.OriginalityText-mining studies targeting research on quality are scarce, and there seem to be no prior models that depict the quality paradigm based on such studies. The perspectives presented here will advance the existing paradigmatic discourse. The new viewpoints aim to facilitate and deepen the discussion on current and future directions of the paradigm.

Identifying new piperazine-based PARP1 inhibitors using text mining and integrated molecular modeling approaches

One of the important molecular targets for antitumor drug discovery is the polyadenosine diphosphate-ribose polymerase-1 (PARP1) enzyme. It is linked with various biological functions including DNA repair and apoptosis. It is primarily a nuclear enzyme linked to chromatin, which is activated by DNA damage. Improved expression of PARP1 in melanomas, breast cancer, lung cancer and other neoplastic diseases is often observed. A tremendous PARP research concerning cancer and ischemia is progressing very rapidly. There are currently four PARP1 inhibitors approved by the FDA on the market, namely Olaparib, Rucaparib, Niraparib and Talazoparib. All of these molecules are non-selective inhibitors of PARP1. Currently there is an urgent need for novel and selective PARP1 inhibitors. In this work, asmall molecule database (Specs SC) were used to identify the new selective lead inhibitors of PARP1. Piperazine scaffold is an important fragment that is used in many currently used FDA approved drugs in different diseases including PARP1 inhibitor Olaparib. Thus, based on text mining studies, 4674 compounds thatinclude piperazine fragments were identified and virtually screened at the binding pocket of target protein PARP1. Compounds that have high docking scores were used in molecular dynamics (MD) simulations. Free energy calculations were also performed to compare the predicted binding energies with known PARP1 inhibitors. The critical amino acid interactions of these newly identified hits in the binding pocket were also investigated in detail for better understanding of the structural features required for next generation PARP1 inhibitors. Thus, here together with combination of text-mining and integrated molecular modeling approaches, we identified novel piperazine-based hits against PARP1 enzyme.Communicated by Ramaswamy H. Sarma

One More Tweet: Firms Challenge a Sustainable Future

In the last decades, researchers have been provided with a huge amount of data thanks to the diffusion of online sources. Additionally, more companies are issuing reports and documents to share information with stakeholders about their sustainable approach to both strengthen and encourage people to adopt a similar approach. To support researchers in managing the increasing quantity of information, several tools have been provided for text mining studies, such as sentiment analysis, semantic analysis, and content analysis. We proposed to analyse the usefulness of automated and semi-automated techniques on a dataset composed of more than 875,000 tweets posted by the companies that Forbes (2020) considers to be the most sustainable. We chose to focus on sustainability because it is a topic of interest to the global community, as revealed by the significant amount of attention that companies are paying to it. In detail, we performed a double-step analysis: firstly, a comparison between exact words and stemmed words; secondly, a description of communication efforts and topics that firms opted for when dealing with sustainability. Our expected contribution is mainly methodological, as we provide suggestions regarding the advantages of performing the analysis in one of the two ways, while the research context offers insights into sustainability

Cross-Cultural Comparisons of Review Aspect Importance

Previous text mining studies identified key common aspects in online restaurant reviews. However, it is not clear how important these aspects are for consumers. In this exploratory study, we used Yelp restaurant reviews on an ethnic food item, ramen noodles, and assessed the importance of each aspect to both U.S. and Japanese consumers. The results show that food and atmosphere are far more important than the other common aspects in both the U.S. and Japan. However, we found noticeable differences between consumers in the two countries regarding how the food aspect plays a role on star ratings. Both implications and a future research agenda are discussed.

The Application of Text Mining and Analytics Studies: A Systematic Literature Review

The Application of Text Mining and Analytics Studies: A Systematic Literature Review

Text mining and semantics: a systematic mapping study

As text semantics has an important role in text meaning, the term semantics has been seen in a vast sort of text mining studies. However, there is a lack of studies that integrate the different research branches and summarize the developed works. This paper reports a systematic mapping about semantics-concerned text mining studies. This systematic mapping study followed a well-defined protocol. Its results were based on 1693 studies, selected among 3984 studies identified in five digital libraries. The produced mapping gives a general summary of the subject, points some areas that lacks the development of primary or secondary studies, and can be a guide for researchers working with semantics-concerned text mining. It demonstrates that, although several studies have been developed, the processing of semantic aspects in text mining remains an open research problem.

A Survey of Text Mining in Social Media: Facebook and Twitter Perspectives

Text mining has become one of the trendy fields that has been incorporated in several research fields such as computational linguistics, Information Retrieval (IR) and data mining. Natural Language Processing (NLP) techniques were used to extract knowledge from the textual text that is written by human beings. Text mining reads an unstructured form of data to provide meaningful information patterns in a shortest time period. Social networking sites are a great source of communication as most of the people in today’s world use these sites in their daily lives to keep connected to each other. It becomes a common practice to not write a sentence with correct grammar and spelling. This practice may lead to different kinds of ambiguities like lexical, syntactic, and semantic and due to this type of unclear data, it is hard to find out the actual data order. Accordingly, we are conducting an investigation with the aim of looking for different text mining methods to get various textual orders on social media websites. This survey aims to describe how studies in social media have used text analytics and text mining techniques for the purpose of identifying the key themes in the data. This survey focused on analyzing the text mining studies related to Facebook and Twitter; the two dominant social media in the world. Results of this survey can serve as the baselines for future text mining research.

Exploring research on quality and reliability management through text mining methodology

PurposeThe purpose of this paper is to explore and describe research presented in the International Journal of Quality & Reliability Management (IJQRM), thereby creating an increased understanding of how the areas of research have evolved through the years. An additional purpose is to show how text mining methodology can be used as a tool for exploration and description of research publications.Design/methodology/approachThe study applies text mining methodologies to explore and describe the digital library of IJQRM from 1984 up to 2014. To structure and condense the data, k-means clustering and probabilistic topic modeling with latent Dirichlet allocation is applied. The data set consists of research paper abstracts.FindingsThe results support the suggestion of the occurrence of trends, fads and fashion in research publications. Research on quality function deployment (QFD) and reliability management are noted to be on the downturn whereas research on Six Sigma with a focus on lean, innovation, performance and improvement on the rise. Furthermore, the study confirms IJQRM as a scientific journal with quality and reliability management as primary areas of coverage, accompanied by specific topics such as total quality management, service quality, process management, ISO, QFD and Six Sigma. The study also gives an insight into how text mining can be used as a way to efficiently explore and describe large quantities of research paper abstracts.Research limitations/implicationsThe study focuses on abstracts of research papers, thus topics and categories that could be identified via other journal publications, such as book reviews; general reviews; secondary articles; editorials; guest editorials; awards for excellence (notifications); introductions or summaries from conferences; notes from the publisher; and articles without an abstract, are excluded.Originality/valueThere do not seem to be any prior text mining studies that apply cluster modeling and probabilistic topic modeling to research article abstracts in the IJQRM. This study therefore offers a unique perspective on the journal’s content.

Research status and trend analysis of global biomedical text mining studies in recent 10 years

In recent years, with the abrupt growth of the amount of biomedical literature, a lot of implicit laws and new knowledge were buried in the vast literature, while the text mining technology, if applied in the biomedical field, can integrate and analyze massive biomedical literature data, obtaining valuable information to improve people’s understanding of biomedical phenomena. This paper mainly discussed the research status of text mining technology applied in the biomedical field in recent 10 years in order to provide a reference for further studies of other researchers. Biomedical text mining literature included in SCI from 2004 to 2013 were retrieved and filtered and then were analyzed from the perspectives of annual changes, regional distribution, research institutions, journals sources, research fields, keywords and so on. The total amount of global biomedical text mining literature is on the rise, among which literature relevant to named entity recognition, entity relation extraction, text categorization, text clustering, abbreviations extraction and co-occurrence analysis take up a large percentage; studies in USA and the UK are in the leading position. Compared with other much more mature research topics, the application of text mining technology in biomedicine is still a relatively new research field worldwide, while with the constantly improving awareness of this field and deepening researches in this area, a number of core research areas, core research institutes and core research fields have been formed in this field. Therefore, further researches of this field will inject new vitality in the development of biomedicine.

SimConcept: A Hybrid Approach for Simplifying Composite Named Entities in Biomedicine.

Many text-mining studies have focused on the issue of named entity recognition and normalization, especially in the field of biomedical natural language processing. However, entity recognition is a complicated and difficult task in biomedical text. One particular challenge is to identify and resolve composite named entities, where a single span refers to more than one concept(e.g., BRCA1/2). Most bioconcept recognition and normalization studies have either ignored this issue, used simple ad-hoc rules, or only handled coordination ellipsis, which is only one of the many types of composite mentions studied in this work. No systematic methods for simplifying composite mentions have been previously reported, making a robust approach greatly needed. To this end, we propose a hybrid approach by integrating a machine learning model with a pattern identification strategy to identify the antecedent and conjuncts regions of a concept mention, and then reassemble the composite mention using those identified regions. Our method, which we have named SimConcept, is the first method to systematically handle most types of composite mentions. Our method achieves high performance in identifying and resolving composite mentions for three fundamental biological entities: genes (89.29% in F-measure), diseases (85.52% in F-measure) and chemicals (84.04% in F-measure). Furthermore, our results show that, using our SimConcept method can subsequently help improve the performance of gene and disease concept recognition and normalization.

Mining the electronic health record for disease knowledge.

The growing amount and availability of electronic health record (EHR) data present enhanced opportunities for discovering new knowledge about diseases. In the past decade, there has been an increasing number of data and text mining studies focused on the identification of disease associations (e.g., disease-disease, disease-drug, and disease-gene) in structured and unstructured EHR data. This chapter presents a knowledge discovery framework for mining the EHR for disease knowledge and describes each step for data selection, preprocessing, transformation, data mining, and interpretation/validation. Topics including natural language processing, standards, and data privacy and security are also discussed in the context of this framework.