Mining Text with the Prototype-Matching Method

Application of Text Mining to Extract Hotel Attributes and Construct Perceptual Map of Five Star Hotels from Online Review: Study of Jakarta and Singapore Five-Star Hotels

A mechanistic model was developed for brand talent demand analysis using text mining techniques in this study. Text mining is a powerful tool for extracting valuable insights from unstructured textual data, and its application in talent demand analysis offers significant potential for strategically informed decision-making in human resource management. The model integrated various technical theories related to text mining, including the Bag of Words model, Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Gibbs sampling. These techniques enable the systematic analysis of textual data to identify patterns and trends in brand talent demand. The basic procedure involved in brand talent demand analysis was constructed using text-mining methodologies. The importance of data collection and preprocessing was emphasized based on web crawlers to gather relevant textual data from online sources. By leveraging web crawlers, the model acquired high-quality datasets essential for accurate analysis. Overall, the proposed mechanistic model provides a framework for brand talent demand analysis and contributes to the advancement of talent management strategies.

Text mining has been used for various purposes, such as document classification and extraction of domain-specific information from text. In this paper we present a study in which text mining methodology and algorithms were properly employed for academic dishonesty (cheating) detection and evaluation on open-ended college exams, based on document classification tech- niques. Firstly, we propose two classification models for cheating detection by using a decision tree supervised algorithm. Then, both classifiers are compared against the result produced by a domain expert. The results point out that one of the classifiers achieved an excellent quality in detecting and evaluating cheating in exams, making possible its use in real school and college environments.

Both the environment and society are fundamentalaspects for businesses. Companies should consider these aspects while addressing challenges like transparency, accountability, sustainability and governance. This study aims at assessing the impact of information systems on Socioenvironmental Auditing Practices (SAP). For that purpose,it assumes that the impact of business activities on society and the environment can be audited, and the efficiency of such auditing practices depends on information systems. Datasets were mined from Scopus and Web of Science (WoS) using text mining methodology and were analyzed using the following tools: Correspondence Analysis (CA) to identify factors, challenges and technologies; Exploratory Factor Analysis (EFA) to identifyunderlying factor structures; Confirmatory Factor Analysis (CFA) to evaluate cause-and-effect relationships among factors, technologies and challenges. A few emerging technologies, viz., big data, blockchain, cloud computing solutions, machine learning, appear to have both a positive and a negative influence on SAP, but these are mostly insignificant. Challenges such as transparency, accountability, and sustainability were found to be associated with SAP. Blockchain technologiesinfluence social and environmental factors positively, whilemachine learning influencesEFAnegativelyyet its impact on CFA is positive and significant

Christian religious language in marketing communications is becoming more common, and many Christian-based service firms communicate religiosity via company websites. The impact of religious language in company websites has not been directly addressed, however. Consequently, using a novel text mining methodology, this research examines the presence and impact of religious language on organizations’ websites over a 10-year period, including the emotional sentiments of the language and their intensity. The most frequently used religious words are identified and discussed, as is how the religious language contributes to the emotional tone of the message.

The current research is a systematic review of the literature on hotel marketing management that provides evidence that hotel marketing management contributes to organizational performance and satisfaction, and provides insights into how hotel operators can successfully implement it. This systematic review of the literature is based on the analysis of 417 papers via a text-mining methodology. Through cluster analysis, we divided the literature on hotel marketing management into four clusters, namely, literature regarding marketing reviews and frameworks, marketing strategies, services, and customers. These results pointed to some potential directions for future research in each cluster. This research can benefit researchers studying the current topics in the hotel marketing management field and help them recognize potential research areas. Additionally, it enables hoteliers to understand the benefits and processes of hotel marketing and defines the key elements of implementing a successful marketing campaign.

The study combined with the text mining and classification methodology to investigate the biofuels related tweets on the social network so as to understand the general public opinion focus. The contribution of the study enclosed the subsequent two points. First, the study utilized the text mining method to explore the content of biofuels connected tweets on the famed social network "twitter" and found the main keywords consistent with their frequencies. Second, the study applied the Back Propagation Neural Network (BPN), random forests model and two sorts of hybrid algorithmclassification analyses to compare the classification results.

Inflammatory Bowel Diseases (IBDs), encompassing Ulcerative Colitis (UC) and Crohn's Disease (CD), are chronic, recurrent inflammatory conditions of the gastrointestinal tract. The microRNA (miRNA) -mRNA regulatory network is pivotal in the initiation and progression of IBDs. Although individual studies provide valuable insights into miRNA mechanisms in IBDs, they often have limited scope due to constraints in population diversity, sample size, sequencing platform variability, batch effects, and potential researcher bias. Our study aimed to construct comprehensive miRNA-mRNA regulatory networks and determine the cellular sources and functions of key miRNAs in IBD pathogenesis. To minimize potential bias from individual studies, we utilized a text mining-based approach on published scientific literature from PubMed and PMC databases to identify miRNAs and mRNAs associated with IBDs and their subtypes. We constructed miRNA-mRNA regulatory networks by integrating both predicted and experimentally validated results from DIANA, Targetscan, PicTar, Miranda, miRDB, and miRTarBase (all of which are databases for miRNA target annotation). The functions of miRNAs were determined through gene enrichment analysis of their target mRNAs. Additionally, we used two large-scale single-cell RNA sequencing datasets to identify the cellular sources of miRNAs and the association of their expression levels with clinical status, molecular and functional alternation in CD and UC. Our analysis systematically summarized IBD-related genes using text-mining methodologies. We constructed three comprehensive miRNA-mRNA regulatory networks specific to IBD, CD, and UC. Through cross-analysis with two large-scale scRNA-seq datasets, we determined the cellular sources of the identified miRNAs. Despite originating from different cell types, hsa-miR-142, hsa-miR-145, and hsa-miR-146a were common to both CD and UC. Notably, hsa-miR-145 was identified as myofibroblast-specific in both CD and UC. Furthermore, we found that higher tissue repair and enhanced glucose and lipid metabolism were associated with hsa-miR-145 in myofibroblasts in both CD and UC contexts. This comprehensive approach revealed common and distinct miRNA-mRNA regulatory networks in CD and UC, identified cell-specific miRNA expressions (notably hsa-miR-145 in myofibroblasts), and linked miRNA expression to functional alterations in IBD. These findings not only enhance our understanding of IBD pathogenesis but also offer promising diagnostic biomarkers and therapeutic targets for clinical practice in managing IBDs.

Implementation of a shared data repository and common data dictionary for fetal alcohol spectrum disorders research

PurposeComputational text mining methods are proposed as a useful methodological innovation in Intimate Partner Violence (IPV) research. Text mining can offer researchers access to existing or new datasets, sourced from social media or from IPV-related organisations, that would be too large to analyse manually. This article aims to give an overview of current work applying text mining methodologies in the study of IPV, as a starting point for researchers wanting to use such methods in their own work.MethodsThis article reports the results of a systematic review of academic research using computational text mining to research IPV. A review protocol was developed according to PRISMA guidelines, and a literature search of 8 databases was conducted, identifying 22 unique studies that were included in the review.ResultsThe included studies cover a wide range of methodologies and outcomes. Supervised and unsupervised approaches are represented, including rule-based classification (n = 3), traditional Machine Learning (n = 8), Deep Learning (n = 6) and topic modelling (n = 4) methods. Datasets are mostly sourced from social media (n = 15), with other data being sourced from police forces (n = 3), health or social care providers (n = 3), or litigation texts (n = 1). Evaluation methods mostly used a held-out, labelled test set, or k-fold Cross Validation, with Accuracy and F1 metrics reported. Only a few studies commented on the ethics of computational IPV research.ConclusionsText mining methodologies offer promising data collection and analysis techniques for IPV research. Future work in this space must consider ethical implications of computational approaches.

<p>In the framework of the EUROVOLC project, Work Package 5 (WP5) consists of a networking activity working towards “consolidation of geochemical gas monitoring across Volcano Observatories”.  This activity promotes the collaboration and cooperation among volcanologists belonging to several research infrastructures (RIs) and Volcanological Observatories (VOs) across Europe and in particular among researchers who undertake geochemical monitoring of volcanic emissions. Eight partners from six different European countries are involved: IMO and UI (Iceland) INGV (Italy), UNILEEDS and UMAN (United Kingdom), CSIC (Spain), CIVISA (Portugal), IPGP and UCA-OPGC (France).</p><p>The study of magmatic degassing in terms of gas chemistry and flux is essential to understand how, and why, volcanoes erupt. Very often, each research group employs different instruments and applies distinct sampling and analytical procedures and strategies, developed from years of experience. One of the consequences of these diverse approaches is the difficulty in comparison of data between the different research groups.</p><p>Based on these challenges, one of the aims of the EUROVOLC project is to define best practices in geochemical gas monitoring for direct sampling of fumaroles, in situ measurements of gas chemistry and remote sensing of volcanic plumes, based on the combined expertise from VOs and RIs, and finalized to optimize the capacity of each VO to monitor the volcanoes they are responsible for. In order to standardize, process, store and share the data collected on volcanic gas emissions, EPOS[BMk1]  (European Plate Observing System) project guidelines are applied.</p><p>Collective field surveys on different volcanic fumaroles and plumes using direct sampling and remote sensing systems have been planned and constitute powerful tools facilitating knowledge and expertise transfer between project partners. In February 2019, we carried out a joint survey at Furnas Volcano in Azores Islands. There, five research groups performed direct sampling on the same low-T fumarole (~100°C), using the procedures followed at each VO. The collected samples were analysed in four different laboratories and the obtained results have been compared in a round-robin test. At the same time, four research groups acquired real-time data of the fumarolic gas using multi-sensor portable instruments produced by different manufactures.</p><p>                                                                                                                                                                   </p><p>A second joint field campaign is scheduled in the late spring of 2020 at Vulcano Island (Italy), where a high temperature (T~300°C) fumarolic field exists. The acquired data will be organized, standardized and stored in a data repository, following common standards so that data for volcanic gas emissions will be accessible to the whole community by implementing the already planned activities in EPOS. The final deliverables include the writing of “user manuals” with standardized recommendations for acquisition of high-quality data for the geochemical monitoring of volcanic gas emissions including fumaroles and plumes, as well as the applicability and limitations of the employed methodology/instrument in different case studies.</p>

With the overwhelming increase in the amount of texts on the web, it is almost impossible for people to keep abreast of up-to-date information. Text mining is a process by which interesting information is derived from text through the discovery of patterns and trends. Text mining algorithms are used to guarantee the quality of extracted knowledge. However, the extracted patterns using text or data mining algorithms or methods leads to noisy patterns and inconsistency. Thus, different challenges arise, such as the question of how to understand these patterns, whether the model that has been used is suitable, and if all the patterns that have been extracted are relevant. Furthermore, the research raises the question of how to give a correct weight to the extracted knowledge. To address these issues, this paper presents a text post-processing method, which uses a pattern co-occurrence matrix to find the relation between extracted patterns in order to reduce noisy patterns. The main objective of this paper is not only reducing the number of closed sequential patterns, but also improving the performance of pattern mining as well. The experimental results on Reuters Corpus Volume 1 data collection and TREC filtering topics show that the proposed method is promising.

Based on a review of 700+ peer-reviewed articles since 1990, identified using text mining methodology and supervised machine learning, we analyze how neo-Schumpeterian growth theorists relate to the entrepreneur-centered view of Schumpeter Mark I and the entrepreneurless framework of Schumpeter Mark II. The literature leans heavily toward Schumpeter Mark II; innovation returns are modeled as following an ex ante known probability distribution. By assuming that innovation outcomes are (probabilistically) deterministic, the entrepreneur becomes redundant. Abstracting from genuine uncertainty, implies that central issues regarding the economic function of the entrepreneur are overlooked such as the roles of proprietary resources, skills, and profits.

Background: While it is commonly recognized that almost every work and research discipline utilize their own taxonomy, the language used within a specific discipline may also vary depending on numerous factors, including the desired effect of the information being communicated and the intended audience. Different audiences are reached through publication of information, including research results, in different types of publication outlets such as newspapers, newsletters, magazines, websites, and journals. Prior research has shown that students, both undergraduate and graduate, as well as faculty may have a difficult time locating information in different publication outlet types (e.g., magazines, newspapers, journals). The type of publication may affect the ease of understanding and also the confidence placed in the acquired information. A text analytics tool for classifying the source of research as a newsletter (used as a substitute for newspaper articles), a magazine, or an academic journal article has been developed to assist students, faculty, and researchers in identifying the likely source type of information and classifying their own writings with respect to these possible publication outlet types. Literature Review: Literature on information literacy is discussed as this forms the motivation for the reported research. Additionally, prior research on using text mining and text analytics is examined to better understand the methodology employed, including a review of the original Scale of Theoretical and Applied Research system, adapted for the current research. Research Questions: The primary research question is: Can a text mining and text analytics approach accurately determine the most probable publication source type with respect to being from a newsletter, magazine, or journal? Methodology: A text mining and text analytics algorithm, STAR’ (System for Text Analytics-based Ranking), was developed from a previously researched text mining tool, STAR (Scale of Theoretical and Applied Research), that was used to classify the research type of articles between theoretical and applied research. The new text mining method, STAR’, analyzes the language used in manuscripts to determine the type of publication. This method first mines all words from corresponding publication source types to determine a keyword corpus. The corpus is then used in a text analytics process to classify full newsletters, magazine articles, and journal articles with respect to their publication source. All newsletters, magazine articles, and journal articles are from the library and information sciences (LIS) domain. Results: The STAR’ text analytics method was evaluated as a proof of concept on a specific LIS organizational newsletter, as well as articles from a single LIS magazine and a single LIS journal. STAR’ was able to classify the newsletters, magazine articles, and journal articles with 100% accuracy. Random samples from another similar LIS newsletter and a different LIS journal were also evaluated to examine the robustness of the STAR’ method in the initial proof of concept. Following the positive results of the proof of concept, additional journal, magazine, and newsletter articles were used to evaluate the generalizability of STAR’. The second-round results were very positive for differentiating journals and newsletters from other publication types, but revealed potential issues for distinguishing magazine articles from other types of publications. Discussion: STAR’ demonstrates that the language used for transferring information within a specific discipline does differ significantly depending on the intended recipients of the research knowledge. Further work is needed to examine language usage specific to magazine articles. Conclusions: The STAR’ method may be used by students and faculty to identify the likely source of research or discipline-specific information. This may improve trust in the reliability of information due to different levels of rigor applied to different types of publications. Additionally, the STAR’ classifications may be used by students, faculty, or researchers to determine the most appropriate type of outlet and correspondingly the most appropriate type of audience for the reported information in their own manuscripts, thereby improving the chance for successful sharing of information to appropriate audiences who will deem the information to be reliable, through publication in the most relevant outlet type.

We recently released a plugin to Pathvisio to assist the curator with automatic integration of heterogeneous pathway knowledge. Pathway curation involves gathering this knowledge not only through discussions with peers, but also from biological databases and the scientific literature. Integrating this all into one pathway is challenging and to date still mainly a laborious manual process.More and more biological databases provide webservices (ie. REST or SOAP). The main objective of webservices is to provide an interface for other computer applications. Our plug-in uses these interfaces to gather binary relationships of a given pathway entity and present the relation in star shaped figures to the curator. The plug-in also uses functionality of Pathvisio to project and visualize research data on pathways. If research data exists, this can then be projected on to the suggested potential novel entities. The curator can then make an educated decision on extending pathways with database knowledge.Different text mining methods exist to process large quantities of available literature. Text mining results can be very valuable in pathway curation. Integrating them in a similar way as with biological databases is difficult. It requires a text-mining output standard. In our current plug-in we included text-mining support in our current version of the plug-in.We like to propose here a standard for text mining output. The output standard is a data object that captures the relationships found with text mining, the document identifiers, the sentences from where the results originate, the method used, and its metrics. Such an output standard would enable a user-oriented comparison of text mining results. Currently, text-mining results are mainly evaluated on their performance in finding relevant results. By enabling a text mining out standard, the results become available to the larger user community. The data is immediately accessible to a wide range of applications. If a text mining method supports our output standard, the pathway curation community can use it in Pathvisio through our plug-in.