Integrated Text Mining Research Articles

Product portfolio decarbonization: Deleting hot products for a cooler supply chain

AbstractModern economic development emphasizes the reduction of carbon emissions. In rationalizing product portfolios, organizations must prioritize low‐carbon development by selecting products with minimal emissions. Eliminating high‐emission items can create opportunities for new low‐carbon products, thereby enhancing competitiveness. In this study, we propose a hybrid mixed method that integrates text mining and multi‐objective optimization algorithms, utilizing a broad range of quantitative and qualitative data sources, including online consumer reviews. We apply this method to a real case in the Chinese air‐conditioning industry for practical validation. The results indicate that for comprehensive product portfolio decarbonization, organizations should consider deleting high‐performing products (e.g., those with strong sales and market performance) to achieve full decarbonization across the entire supply chain. This approach offers valuable insights for managers aiming to simultaneously improve economic and environmental performance.

A Comprehensive Analysis of the per- and poly-fluoroalkyl substances (PFAS) research landscape through AI-assisted text mining

Per- and poly-fluoroalkyl substances (PFAS) have been widely used in various industrial applications due to their unique properties. This study aims to provide a comprehensive analysis of PFAS research trends using a novel approach combining text mining techniques and large-scale language models (LLMs). PFAS-related scientific literature published from 1980 to 2024 was gathered from Scopus, and KH Coder and Claude 3 were used to perform the analysis. The results showed a significant increase in research output and a clear shift in research topics over the past 40 years. Whereas in the past, the focus was on analytical methods, more recently, the emphasis has been on environmental fate, toxicity assessment, alternative compounds, and regulation. With Claude 3, research areas can now be identified without reviewing the results of expert text mining. Comparisons of AI-extracted trends with insights from traditional review articles showed strong agreement, confirming the effectiveness of this approach. These findings suggest the need for continued interdisciplinary research on PFAS such as the development of remediation strategies, elucidation of health effects, and evidence-based policymaking. This study showed the possibility of integrating text mining and LLM for a comprehensive analysis of research trends, which will accelerate future research and development strategies.

Great remains great, weak becomes weaker? Examining the influence of platform’s signaling cues on provider performance

In the evolving domain of access-based services characterized by intricate multi-actor interactions, understanding the determinants of provider performance is vital. This study investigates the moderating role of platform-based signaling cues on the relationship between customer experience (CX) and provider performance. We utilize a mixed-methods approach, integrating text mining to quantify CX from customer reviews and panel data fixed-effects regression to assess performance outcomes. Our findings reveal an asymmetry in the impact of CX based on platform signals: non-signaled providers suffer more from negative experiences than their signaled counterparts, while positive experiences do not exhibit a differential impact. Interestingly, customer-reviewed ratings dampen the adverse effects of negative CX, serving as a substitute for platform endorsement. Moreover, high dispersion in customer feedback enhances the benefits of positive CX for signaled providers. These insights offer actionable strategies for access-based companies to optimize customer touchpoints and foster enhanced service delivery.

Text mining on sustainability reports of top 40 airlines and bibliometric analysis of airline's sustainability

This study aims to comprehensively explore sustainability practices, guidelines, and emerging trends in the airline industry through an in-depth analysis of their sustainability reviews. A thorough bibliometric analysis of airline sustainability was conducted using the Scopus Database. Additionally, employing text-mining techniques, a meticulous analysis focused on the sustainability reports of the leading 20 airlines compared to the subsequent 20, considered as followers, in the textual examination of sustainability reports. The results revealed thematic disparities between these two cohorts. The top 20 airlines prioritized significant concerns such as safety and other sustainability-related aspects like biodiversity. In contrast, follower companies placed a higher emphasis on financial considerations. This analysis illuminates the primary focuses of airlines and the spectrum of sustainability-related issues. Moreover, offering valuable insights for both researchers and industry practitioners, this study presents a repository of pertinent data related to sustainability practices in the aviation sector. Integrating text mining and bibliometric analysis emphasizes essential facets of airline sustainability, resulting in a comprehensive overview of the research landscape.

Precision Drug Repurposing: A Deep Learning Toolkit for Identifying 34 Hyperpigmentation-Associated Genes and Optimizing Treatment Selection.

Hyperpigmentation is a skin disorder characterized by a localized darkening of the skin due to increased melanin production. When patients fail first line topical treatments, secondary treatments such as chemical peels and lasers are offered. However, these interventions are not devoid of risks and are associated with postinflammatory hyperpigmentation. In the quest for novel therapeutic potentials, this study aims to investigate computational methods in the identification of new targeted therapies in the treatment of hyperpigmentation. We used a comprehensive approach, which integrated text mining, interpreting gene lists through enrichment analysis and integration of diverse biological information (GeneCodis), protein-protein association networks and functional enrichment analyses (STRING), and plug-in network centrality parameters (Cytoscape) to pinpoint genes closely associated with hyperpigmentation. Subsequently, analysis of drug-gene interactions to identify potential drugs (Cortellis) was utilized to select drugs targeting these identified genes. Lastly, we used Deep Learning Based Drug Repurposing Toolkit (DeepPurpose) to conduct drug-target interaction predictions to ultimately identify candidate drugs with the most promising binding affinities. Thirty-four hyperpigmentation-related genes were identified by text mining. Eight key genes were highlighted by utilizing GeneCodis, STRING, Cytoscape, gene enrichment, and protein-protein interaction analysis. Thirty-five drugs targeting hyperpigmentation-associated genes were identified by Cortellis, and 29 drugs, including 16 M2PK1 inhibitors, 11 KRAS inhibitors, and 2 BRAF inhibitors were recommended by DeepPurpose. The study highlights the promise of advanced computational methodology for identifying potential treatments for hyperpigmentation.

Empowering healthcare with NLP-driven deep learning unveiling biomedical materials through text mining

This study presents a comprehensive approach to automated biomedical materials discovery in healthcare applications by integrating text mining and deep learning techniques. The research methodology encompasses two main components: exploration of key research questions through graphical representation and evaluation of model performance using precision, recall, and F1-score metrics. The identification of pertinent research questions is visualized using bar charts, offering insights into the distribution of studies across domains such as data harmonization, heterogeneity, industrial textual data, and sequential data performance. The precision comparison chart highlights the strengths and weaknesses of different models, with model 1 demonstrating notable precision. The recall comparison chart emphasizes model 2’s outstanding performance in capturing relevant information, while the f1-score comparison chart showcases the balanced metrics of model 2 and 4. These visual analyses contribute to a nuanced understanding of the research landscape and guide the development of the proposed NLP-ML pipeline. The study’s findings underscore the significance of addressing data harmonization challenges and extracting insights from industrial textual data in advancing biomedical materials discovery. Overall, this research amalgamates exploratory data analysis and quantitative model evaluation to contribute to the evolving field of text mining and deep learning applications in biomedical material discovery for healthcare applications.

A Thematic Analysis of English and American Literature Works Based on Text Mining and Sentiment Analysis

A theme analysis model integrating text mining and sentiment analysis has emerged as a powerful tool for understanding English and American literary works. By employing techniques such as topic modeling, keyword extraction, and sentiment analysis, this model can identify recurring themes, motifs, and emotional tones within texts. Through text mining, it extracts key concepts and topics, while sentiment analysis discerns the underlying emotions conveyed by the authors. By combining these approaches, researchers can uncover deeper insights into the thematic elements and cultural contexts of English and American literature. This paper explores the application of text mining and sentiment analysis techniques to analyze a dataset comprising American literary works. With computational methods such as bi-gram analysis, multimodal feature extraction, and sentiment analysis using the Bi-gram Multimodal Sentimental Analysis (Bi-gramMSA) approach. With the proposed Bi-gramMSA model the multimodal features in the American Literature are examined to investigate the thematic, emotional, and multimodal aspects of the literature. Through our analysis, we uncover significant bi-grams, extract multimodal features, and assess sentiment distribution across the texts. The results highlight the effectiveness of these computational methodologies in uncovering patterns, sentiments, and features within the literary corpus. The proposed Bi-gramMSA model achives a higher score for the different scores in the Chinese Literature.

Navigating the new normal: Redefining N95 respirator design with an integrated text mining and quality function deployment-based optimization model

The global surge in demand during the pandemic led to a proliferation of substandard surgical N95 respirators, particularly in the e-commerce market, due to a lack of regulatory standards. In the post-pandemic era, the essential role of these respirators in protecting against airborne hazards and infectious agents persists. Addressing the need for standardized design and manufacturing processes, this research proposes a three-phased integrated decision-making framework. Firstly, text-mining algorithms analyze online consumer reviews to identify critical characteristics of surgical N95 respirators. Subsequently, a spherical-fuzzy-based quality function deployment assesses manufacturing capabilities in alignment with consumer requirements. The final phase utilizes Mixed Integer Non-linear Programming to optimize utility and select the most effective design, considering both technical specifications and consumer needs. The proposed framework is validated through a case study, revealing technical characteristics crucial for organizations in product development. The findings not only offer insights into the technical aspects of N95 respirators but also recommend essential product characteristic tests during manufacturing and certification. This information is vital for educating consumers and guiding companies in the effective use and development of Surgical N95 respirators in the post-pandemic landscape, addressing the challenges posed by inferior and counterfeit products.

Development and application of Chinese medical ontology for diabetes mellitus

ObjectiveTo develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies.Materials and methodsWe used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering.ResultsThe current CDMO consists of 3,752 classes, 182 fine-grained object properties with hierarchical relationships, 108 annotation properties, and over 12,000 mappings to other well-known medical ontologies in English. Based on the CDMO and clinical practice guidelines, we developed 200 rules for diabetes diagnosis, treatment, diet, and medication recommendations using the Semantic Web Rule Language. By injecting ontology knowledge, CDMO enhances the performance of the T5 model on a real-world Chinese medical question answering dataset related to diabetes.ConclusionCDMO has fine-grained semantic relationships and extensive annotation information, providing a foundation for medical artificial intelligence applications in Chinese contexts, including the construction of medical knowledge graphs, clinical decision support systems, and automated medical question answering. Furthermore, the development process incorporated natural language processing and cross-lingual ontology mapping to improve the quality of the ontology and improved development efficiency. This workflow offers a methodological reference for the efficient development of other high-quality Chinese as well as non-English medical ontologies.

Integrating text mining with network models for successful target identification: in vitro validation in MASH-induced liver fibrosis.

An in silico target discovery pipeline was developed by including a directional and weighted molecular disease network for metabolic dysfunction-associated steatohepatitis (MASH)-induced liver fibrosis. This approach integrates text mining, network biology, and artificial intelligence/machine learning with clinical transcriptome data for optimal translational power. At the mechanistic level, the critical components influencing disease progression were identified from the disease network using in silico knockouts. The top-ranked genes were then subjected to a target efficacy analysis, following which the top-5 candidate targets were validated in vitro. Three targets, including EP300, were confirmed for their roles in liver fibrosis. EP300 gene-silencing was found to significantly reduce collagen by 37%; compound intervention studies performed in human primary hepatic stellate cells and the hepatic stellate cell line LX-2 showed significant inhibition of collagen to the extent of 81% compared to the TGFβ-stimulated control (1μM inobrodib in LX-2 cells). The validated in silico pipeline presents a unique approach for the identification of human-disease-mechanism-relevant drug targets. The directionality of the network ensures adherence to physiologically relevant signaling cascades, while the inclusion of clinical data boosts its translational power and ensures identification of the most relevant disease pathways. In silico knockouts thus provide crucial molecular insights for successful target identification.

Enhancing safety of construction workers in Korea: an integrated text mining and machine learning framework for predicting accident types

Construction workers face a high risk of various occupational accidents, many of which can result in fatalities. This study aims to develop a prediction model for nine prevalent types of construction accidents, utilizing construction tasks, activities, and tools/materials as input features, through the application of machine learning-based multi-class classification algorithms. 152,867 construction accident summary reports, composed of both structured (construction task, construction activity, accident type) and unstructured data (tools/materials) were used for the study. The study employed several data processing techniques, including keyword extraction through text mining, Boruta feature selection, and SMOTE data resampling enhance model accuracy. Three performance metrics (Multi-class area under the receiver operating characteristic curve (MAUC), Multi-class Matthews Correlation Coefficient (MMCC), Geometric-mean (G-mean)) were used to compare the predictive performance of four machine learning algorithms, including Decision tree, Random forest, Naïve bayes, and XGBoost. Of the four algorithms, XGBoost showed the highest performance in predicting accident type (MAUC: 0.8603, MMCC: 0.3523, G-mean: 0.5009). Furthermore, a Shapley additive explanation (SHAP) analysis was conducted to visualize feature importance. The findings of this study make a valuable contribution to improving construction safety by presenting a prediction model for accident types derived from real-world big data.

The Application of Text Mining Technology in Business Management—Take Hotel Management as an Example

In recent years, the development of the information industry has led to the rapid rise of data mining, and in the face of the trend of data mining development, the field of data mining has become a focal point of concern for various industries. As a very customer-focused service industry, the hospitality industry is imperative to popularize and intelligently integrate text mining into management in the context of rising human resource costs and the general intelligence of all industries. In this paper, the application of text mining technology in business management is explored, using hotel management as an example. The Porter's Five Forces model, the SWOT model are used to analyze the importance of unstructured data such as customer reviews and the great role of text analytics in the internal and external management of hotels. The recommendations for the integration of text mining and hotel management are proposed, suggesting that text mining should be commonly used in hotel management and recommending that managers adopt A small amount of manpower and a large amount of text mining to overcome the drawbacks of text mining and improve hotel operations.

Integrating text mining and system dynamics to evaluate financial risks of construction contracts

Integrating text mining and system dynamics to evaluate financial risks of construction contracts

A novel MICMAC approach for cross impact analysis with application to urban water/wastewater management

Water as the core of Sustainable Development (SD) alongside a significant increase in the global demand for water causes the subject of Urban Water and Wastewater (U2W) management to be essential in exploring the future. As a means of future studies, MICMAC reveals the influence and dependence of a variable concerning all others by examining all interactions in the system. These interactions should be analyzed in two dimensions: “Impact” and “Time”. Usually, two types of impacts are used, including direct and indirect. Also, most methods have a critical weakness in that it does not include the impact of “time” in the analysis.This paper suggests a novel approach to MICMAC (named MICMAC-T), which promotes understanding indirect cross impacts by considering the influence of “time” in analysis. This helps in particular with U2W, which faces major future challenges toward SD. The practical approach developed in this paper can be quickly and flexibly initiated because of its toolbox-based design, integrating text mining, PESTEL, MICMAC, Floyd-Warshall, and interviews in a single model. Therefore, the proposed method could also be considered a new contribution to the CIA.To demonstrate the contribution of MICMAC-T, the case of the Iranian U2W pathway toward SD is investigated to find the driving forces. The results of applying this approach in finding the driving forces for Iranian U2W include new interesting findings. The results of MICMAC-T suggest that “Legal comprehensiveness,” “Urban economic development policies,” “High-level policy documents,” “Public social awareness in society,” “Geographical location (climate),” “Law enforcement guarantee,” “U2W governance”, and “Urban water resources scarcity” are driving forces for the Iranian U2W. Among these variables, “Urban economic development policies” and “Public social awareness in society” were neglected in traditional MICMAC and categorized as driving forces only using MICMAC-T, which indicates the crucial influence of time on Iranian U2W.

Integrating text mining and analytic hierarchy process risk assessment with knowledge graphs for operational risk analysis

Integrating text mining and analytic hierarchy process risk assessment with knowledge graphs for operational risk analysis

Towards a Dedicated Social Media and Groupware for Effective Communication during the Nyiragongo Volcanic Crisis

The issue of natural disasters, their management, and their prevention, requires good communication among stakeholders and between stakeholders and the population. The Nyiragongo volcanic crisis was a traumatic experience for those who were in Goma city in 2021, on 22 and 23 May. It was characterized by insufficient communication, demonstrating the lack of cohesion between stakeholders. In order to prepare a possible implementation of dedicated social media and groupware for effective communication during the Nyiragongo Volcanic Crisis, this paper presents a survey conducted among Goma citizens and proposes a possible architecture of a dedicated social media integrating text mining, which would help develop a flexible evacuation plan. Based on the results obtained, it appears that the establishment of a single information system related to the volcanic crisis can reduce the challenges related to social media where the volcano observatory, political authorities, different partners and citizens can share alert messages, locations of danger, and more comments. This unified information system would help to gather, discuss and disseminate accurate information on volcanic activities and associated hazards and risks.

Assessing cognitive presence in online inquiry‐based discussion through text classification and epistemic network analysis

AbstractProviding coaching to participants in inquiry‐based online discussions contributes to developing cognitive presence (CP) and higher‐order thinking. However, a primary issue limiting quality and timely coaching is instructors' lack of tools to efficiently identify CP phases in massive discussion transcripts and effectively assess learners' cognitive development. This study examined a computational approach integrating text mining and co‐occurrence analysis for assessing CP and cognitive development in online discussions based on the community of inquiry (CoI) framework. First, text classifiers trained on different language models were evaluated for identifying and coding the CP phases. Second, epistemic network analysis (ENA) was employed to model cognitive patterns reflected by co‐occurrences between the coding elements. Results indicated that text classifiers trained on the state‐of‐the‐art language model Bidirectional Encoder Representations from Transformers (BERT) can address the efficiency issue in coding CP phases in discussion transcripts and obtain substantial agreements (Cohen's k = 0.76) with humans, which outperformed other baseline classifiers. Furthermore, compared to traditional quantitative content analysis, ENA can effectively model the temporal characteristics of online discourse and detect fine‐grained cognitive patterns. Overall, the findings suggest a feasible path for applying learning analytics to tracking learning progression and informing theory‐based assessments. Practitioner notesWhat is already known about this topic Cognitive presence is an important construct describing the progression of thinking in online inquiry‐based discussions. Most studies used self‐report instruments or quantitative content analysis to measure and assess cognitive presence. More efficient and effective approaches were needed by instructors to support assessment of cognitive development and determine coaching strategies. What this paper adds An integrated computational approach for the developmental and formative assessment of cognitive presence was proposed and evaluated. A BERT‐based text classification model could efficiently code massive transcripts and achieve substantial agreements with human coders. Epistemic network analysis effectively revealed the process of cognitive development and identified representative discussion patterns and behaviours. Implications for practice and/or policy The proposed approach can considerably reduce the pressure on instructors, enabling them to focus on quality coaching and feedback. Compared to frequencies of individual codes, the connective features between codes carry more insights for assessing cognitive patterns. Learners in a discussion group play different roles and produce diverse paths of cognitive development.

The use of integrated text mining and protein-protein interaction approach to evaluate the effects of combined chemotherapeutic and chemopreventive agents in cancer therapy.

Combining chemotherapeutic (CT) and chemopreventive (CP) agents for cancer treatment is controversial, and the issue has not yet been conclusively resolved. In this study, by integrating text mining and protein-protein interaction (PPI), the combined effects of these two kinds of agents in cancer treatment were investigated. First, text mining was performed by the Pathway Studio database to study the effects of various agents (CP and CT) on cancer-related processes. Then, each group's most important hub genes were obtained by calculating different centralities. Finally, the results of in silico analysis were validated by examining the combined effects of hesperetin (Hst) and vincristine (VCR) on MCF-7 cells. In general, the results of the in silico analysis revealed that the combination of these two kinds of agents could be useful for treating cancer. However, the PPI analysis revealed that there were a few important proteins that could be targeted for intelligent therapy while giving treatment with these agents. In vitro experiments confirmed the results of the in silico analysis. Also, Hst and VCR had good harmony in modulating the hub genes obtained from the in silico analysis and inducing apoptosis in the MCF-7 cell line.

The bi-objective orienteering problem with hotel selection: an integrated text mining optimisation approach

The bi-objective orienteering problem with hotel selection: an integrated text mining optimisation approach

Integrating Text Mining into the Curation of Disease Maps.

An adequate visualization form is required to gain an overview and ultimately understand the complex and diverse biological mechanisms of diseases. Recently, disease maps have been introduced for this purpose. A disease map is defined as a systems biological map or model that combines metabolic, signaling, and physiological pathways to create a comprehensive overview of known disease mechanisms. With the increase in publications describing biological interactions, efforts in creating and curating comprehensive disease maps is growing accordingly. Therefore, new computational approaches are needed to reduce the time that manual curation takes. Test mining algorithms can be used to analyse the natural language of scientific publications. These types of algorithms can take humanly readable text passages and convert them into a more ordered, machine-usable data structure. To support the creation of disease maps by text mining, we developed an interactive, user-friendly disease map viewer. The disease map viewer displays text mining results in a systems biology map, where the user can review them and either validate or reject identified interactions. Ultimately, the viewer brings together the time-saving advantages of text mining with the accuracy of manual data curation.