Application Of Text Mining Techniques Research Articles

Text Mining-Based Study on Consumer Satisfaction in the Mobile Phone Market

In the current context of rapid technological advancement, smartphones have become an indispensable part of people's daily lives. This has led to an increasing focus on the satisfaction of consumers with smartphone products, as understanding consumer emotions and satisfaction has become a key factor for manufacturers and retailers to enhance the quality of products and services. This study delves into the satisfaction of consumers with smartphones in the market through an in-depth application of text mining techniques, leveraging advanced technologies such as natural language processing, sentiment analysis, and topic modeling. Our research methodology encompasses the process of collecting and preprocessing a substantial volume of consumer reviews from online shopping platforms. Subsequently, we apply Latent Dirichlet Allocation (LDA) for topic modeling and Extreme Learning Machine (ELM) for sentiment analysis.

Application of text mining techniques to identify actual wrong-way driving (WWD) crashes in police reports

Application of text mining techniques to identify actual wrong-way driving (WWD) crashes in police reports

Analysis and Comparison of Limitation Interests in Civil Procedure Law in the Context of Information Technology

Abstract This paper first discusses the application of text mining techniques in public security, with the research focus on case classification, feature extraction, and collusion analysis in civil litigation. A plain Bayesian-based first-level classifier is proposed to accurately classify first-level case texts. For all second-level case category document sets, keyword cooccurrence mapping is used to replace word frequency as a metric for feature selection, and a second-level classifier is constructed using a simple vector distance algorithm. Finally, the impact of time-barred benefit abandonment and time-barred benefit loss on the evolutionary system in civil litigation is investigated in depth using the pilot water tax reform in Hunan Province as a case study. The results show that when one of the subjects chooses to discard the statute of limitations benefits, x and y show a trend of steadily increasing convergence to 1 from the initial point, and the final stabilization point tends to (1,1). This paper aims to provide references and suggestions for reforming information and optimizing civil litigation.

Study of violence against women and its characteristics through the application of text mining techniques

Study of violence against women and its characteristics through the application of text mining techniques

Determining Research Trends in the Field of Cross-Border E-Commerce (CBEC) Utilizing Text Mining Techniques

Purpose - This paper seeks to analyze numerous abstracts associated with Cross Border E-commerce (CBEC) through the application of text mining techniques. The goal is to pinpoint key research trends and forecast upcoming developments. By delving into the existing literature and spotting emerging trends, this study aims to offer invaluable insights into the present state of research in the domain, and underscore areas warranting further exploration.
 Design/Methodology/Approach - In this study, text mining techniques were employed to analyze research trends in CBEC. Text mining can distill the core content of a document by analyzing keywords. The analysis encompassed 768 CBEC research articles published in journals indexed by SCI, SCIE, SSCI, and SCOPUS.
 Findings - After conducting research on CBEC using text mining techniques, this paper identified the primary topics of ‘Convergence Research’, ‘International Logistics’, and ‘China CBEC’. The study suggests that there is an expected transition from a straightforward e-commerce distribution model to a structure that converges with other industries.
 Research Implications - Given the complexities of international commerce, it is crucial to offer product information that aligns with the customs and culture of the target country. This study suggests that CBEC will likely evolve by integrating IT technologies, such as big data and AI, with international logistics. As a result, it is anticipated to emerge as a converging industry.

Applications of Text Mining in the Transportation Infrastructure Sector: A Review

Transportation infrastructure is vital to the well-functioning of economic activities in a region. Due to the digitalization of data storage, ease of access to large databases, and advancement of social media, large volumes of text data that relate to different aspects of transportation infrastructure are generated. Text mining techniques can explore any large amount of textual data within a limited time and with limited resource allocation for generating easy-to-understand knowledge. This study aims to provide a comprehensive review of the various applications of text mining techniques in transportation infrastructure research. The scope of this research ranges across all forms of transportation infrastructure-related problems or issues that were investigated by different text mining techniques. These transportation infrastructure-related problems or issues may involve issues such as crashes or accidents investigation, driving behavior analysis, and construction activities. A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)-based structured methodology was used to identify relevant studies that implemented different text mining techniques across different transportation infrastructure-related problems or issues. A total of 59 studies from both the U.S. and other parts of the world (e.g., China, and Bangladesh) were ultimately selected for review after a rigorous quality check. The results show that apart from simple text mining techniques for data pre-processing, the majority of the studies used topic modeling techniques for a detailed evaluation of the text data. Other techniques such as classification algorithms were also later used to predict and/or project future scenarios/states based on the identified topics. The findings from this study will hopefully provide researchers and practitioners with a better understanding of the potential of text mining techniques under different circumstances to solve different types of transportation infrastructure-related problems. They will also provide a blueprint to better understand the ever-evolving area of transportation engineering and infrastructure-focused studies.

Literature Mining of Disease Associated Noncoding RNA in the Omics Era.

Noncoding RNAs (ncRNA) are transcripts without protein-coding potential that play fundamental regulatory roles in diverse cellular processes and diseases. The application of deep sequencing experiments in ncRNA research have generated massive omics datasets, which require rapid examination, interpretation and validation based on exiting knowledge resources. Thus, text-mining methods have been increasingly adapted for automatic extraction of relations between an ncRNA and its target or a disease condition from biomedical literature. These bioinformatics tools can also assist in more complex research, such as database curation of candidate ncRNAs and hypothesis generation with respect to pathophysiological mechanisms. In this concise review, we first introduced basic concepts and workflow of literature mining systems. Then, we compared available bioinformatics tools tailored for ncRNA studies, including the tasks, applicability, and limitations. Their powerful utilities and flexibility are demonstrated by examples in a variety of diseases, such as Alzheimer’s disease, atherosclerosis and cancers. Finally, we outlined several challenges from the viewpoints of both system developers and end users. We concluded that the application of text-mining techniques will booster disease-associated ncRNA discoveries in the biomedical literature and enable integrative biology in the current omics era.

Deep transitions: A mixed methods study of the historical evolution of mass production

• We develop a novel mixed method approach for studying the evolution of rules. • Evolution of mass production confirms the propositions of Deep Transitions theory. • Crisis of a mature meta-regime stimulates its internal transformation. • Text mining shows promise for the study of complex long-term multi-system change. Industrial societies contain a range of socio-technical systems fulfilling functions such as the provision of energy, food, mobility, housing, healthcare, finance and communications. The recent Deep Transitions (DT) framework outlines a series of propositions on how the multi-system co-evolution over 250 years of these systems has contributed to several current social and ecological crises. Drawing on evolutionary institutionalism, the DT framework places a special emphasis on the concepts of ‘rules’ and ‘meta-rules’ as coordination mechanisms within and across socio-technical systems. In this paper, we employ a mixed-method approach to provide an empirical assessment of the propositions of the DT framework. We focus on the historical evolution of mass production from the 18th century to the present. Combining a qualitative narrative based on a synthesis of secondary historical literature with a quantitative text mining-based analysis of the corpus of Scientific American (1845–2019), we map the emergence and alignment of rules underpinning mass production. Our study concludes by reflecting on important methodological lessons for the application of text mining techniques to examine large-scale and long-term socio-technical dynamics.

Exploring Information Systems Curricula

The study considers the application of text mining techniques to the analysis of curricula for study programs offered by institutions of higher education. It presents a novel procedure for efficient and scalable quantitative content analysis of module handbooks using topic modeling. The proposed approach allows for collecting, analyzing, evaluating, and comparing curricula from arbitrary academic disciplines as a partially automated, scalable alternative to qualitative content analysis, which is traditionally conducted manually. The procedure is illustrated by the example of IS study programs in Germany, based on a data set of more than 90 programs and 3700 distinct modules. The contributions made by the study address the needs of several different stakeholders and provide insights into the differences and similarities among the study programs examined. For example, the results may aid academic management in updating the IS curricula and can be incorporated into the curricular design process. With regard to employers, the results provide insights into the fulfillment of their employee skill expectations by various universities and degrees. Prospective students can incorporate the results into their decision concerning where and what to study, while university sponsors can utilize the results in their grant processes.

An application of text mining techniques and outcome based education: student recruitment system

Outcome-based education (OBE) is one of the student-centric teaching and learning methodology. The primary objective of the outcome-based education is measuring the student performance through outcomes. Course outcome (CO) reflects the abilities of what a student can do at the end of a course. Assessment of learning outcome is one of the key aspects of teaching-learning process. The attainment of course outcome will be used to improve the teaching, learning process and to evaluate the student’s performance in that course. In this paper, we propose a new student recruitment system to identify the best students based on their Course Outcome attainment. We apply the text mining concept in this teaching learning process to find the best student list based on the recruiter query. The student recruitment process involved with many parameters like technical skill and analytical skill. The quality of the student’s technical skill can be measured by his/her performance in the CO attainment of a particular course or program. This proposed method utilizes data obtained from student’s marks in end semester exams, test, project and other formal assessments.

Text Mining for Supply Chain Risk Management in the Apparel Industry

Text mining tools are now widely used for the efficient management of information and resources in business, academic and research organizations. This paper provides a comprehensive overview of research articles on the application of text mining techniques in the field of Supply Chain Risk Management and the apparel industry. Research articles published between 2000 and 2020, were obtained from various journals through two online databases, i.e., SCOPUS and IEEE Xplore. Through a systematic approach following PRISMA guidelines, 370 research papers were screened, filtered and finally classified into three main areas: Supply Chain Risk Management and outsourcing in the apparel industry, application of text mining in Supply Chain Risk Management and application of text mining in the apparel industry. In this study, we have identified a comprehensive list of various available data sources for text mining, methodologies and risks associated with outsourcing in the apparel industry. We classify the gaps in expanding the application of text mining in the apparel industry’s Supply Chain Risk Management. Extracting useful information from online newspapers through text mining could vividly enhance the ability to monitor supply chain risks and provide the ability to link data to provide decision makers with the right information at the right time.

Correlations between Information Science research groups in Brazil: an approach based on keywords

Analyzing correlations between research groups has been increasingly appealing in recent years. The identification of proximity between different research projects can not only contribute to triggering new partnerships, but also optimize resources and share results. In Brazil, the Lattes Curriculum System of the Brazilian National Council for Scientific and Technological Development is a rich source of information about the academic and professional life of professors, researchers, and students. Lattes curricula present information, much of it up-to-date, in a semi-structured text format. This paper intends to identify correlations between Brazilian research groups in Information Science through the analysis of keywords contained in the informative summaries and in the descriptions of the research projects found in the Lattes curricula of the participants of these groups. The analysis presented below was made with the application of text mining techniques to the Lattes curricula of researchers linked to 27 graduate programs in Information Science from 24 Brazilian institutions of higher education, totaling 399 curricula analyzed. Among the results obtained, it was possible to identify some existing research trends between the groups and link them to the areas of Information Science, Archivology, Library Science, and Museology. It was also possible to identify the most used research terms at the moment. In addition, the analysis of the occurrence of the terms allowed to identify the areas that concentrate most of the research in Information Science in Brazil, as well as to realize that there is a propensity of researchers to use certain terms to describe their research and their informative summaries.

ArchiText Mining: primjena tekstualne analitike na istraživanje moderne arhitekture

ArchiteXt Mining: Spanish Modern Architecture through Its Texts (1939–1975) is a research project funded by the Government of Spain through the 2015 Call for “Excellence Projects” of the Ministry of Economy and Competitiveness. This project aims to explore a new viewpoint and look into the special features of Spanish modern architecture. Despite the increasing success of using data analysis as a tool in a variety of disciplines, research on architectural theory has never made the most efficient use of these technologies. The Spanish and international circumstances of modern architecture development have been scrutinized through qualitative research, which has established a shared theoretical ground. It is now time to start a new in-depth research based on objective data. To address this challenge, we propose the application of text mining techniques to take advantage of the best data source in the field: architectural periodicals. The purpose is to create a powerful database hosted on a public website for the scientific community. Thus, this project fulfils several e-Research objectives: to facilitate the computerization of data research, to support every stageof data collection, and to manage big data analyses with thehelp of specific tools.

A Study on Automatic Assignment of Standard Classification Numbers to e-Records with the Machine Learning Approach

Belge üretimindeki artış ve teknolojik olanaklar beraberinde yeni yönetim metotlarının gerekliliğini kaçınılmaz kılmıştır. Türkiye’de kamu kuruluşlarında üretilen belgeler Standart Dosya Planı’na uygun olarak düzenlenir ve yönetilirler. İlgili mevzuata koşut olarak resmi yazışmaların konusu Dosya Planından saptanmak ve konuyla bağlantılı kodları belgelere eklemek zorunluluktur. Bu kodların doğru seçilmesi, araştırma-soruşturma süreçlerinin sağlıklı işletilebilmesi ve erişim süreçlerinin başarılı şekilde sonuçlandırılabilmesi için oldukça gereklidir. Ancak kurumsal, kişisel veya yönetsel koşullara bağlı olarak belgelerin yaşam döngüsünü sekteye uğratacak yanlış kodlar verilebilmektedir. Bu tür yanlış uygulamaları minimize etmek ve belge sınıflandırmayı daha sağlıklı kılabilmek için yapay zekâ uygulamalarından yararlanılabilir.Elektronik belge yönetimi sistemlerinde üretilen belgelere standart dosya plan kodlarının makine öğrenmesi yaklaşımıyla otomatik olarak atanması amaçlanan bu çalışma teorik ve analize dayalı olmak üzere iki kısımdan oluşmaktadır. İlkin teorik olarak standart dosya planından yararlanarak otomatik belge sınıflandırmasının oluşturduğu güçlükler tartışılmış, ardından makine öğrenmesi ile belgelerin sınıflandırılması üzerine analiz yapılmıştır. Çeşitli yönetsel ve ön yargısal bariyerlerin aşılmaması ve yanı sıra kurumsal arşiv gibi otorite bir birimin olmaması belge yönetimi, eğitimi ve denetimi boşluğunu oluşturduğu ve söz konusu bu durumunun otomatik sınıflamayı sekteye uğratacağı kaygısı, dolayısıyla belgeleri yeniden sınıflandırma gerekliliği küçük bir veri kümesi ile çalışmayı zorunlu kılmıştır. Bu nedenle çalışmada analiz edilen belgeler, bu çalışmanın araştırmacısına kurum içerisinde yönlendirilen son altı aylık belgelerden oluşmaktadır. Toplamda 265 belgenin yeniden sınıflandırılması neticesinde tekil konudaki belgeler kapsam dışı bırakılmıştır. Belgelerin gövde ve konu alanları üzerinde yapılan metin madenciliği teknikleri uygulanması sonucunda, 169 belgeden oluşan bir veri seti elde edilmiştir. Bu veri setinden her konudan oransal olmak koşuluyla rastgele yöntemle belgelerin üçte biri (1/3) sınıflandırmak için seçilmiştir. Sınıflandırılmış 112 belge ve sınıflandırmak üzere oluşturulmuş 57 belgeden ibaret bu veri seti üzerinde, makine öğrenmesinde kullanılan ve son zamanlarda bilgi sektöründe popüler olan Destek Vektör Makinesi [DVM (Support Vector Machine (SVM)] algoritması çalıştırılmıştır. Çalışma sonucunda manuel olarak yapılan sınıflama ile otomatik olarak yapılan çıkarımın isabet oranı % 87.72 olarak bulunmuştur. Bir diğer ifade ile belgelerin % 87.72’si makine öğrenmesi yaklaşımıyla doğru olarak sınıflanmıştır.

CYBERBULLYING IN E-LEARNING PLATFORM: AN EXPLORATORY STUDY THROUGH TEXT MINING ANALYTICS

The adoption and use of Learning Management System (LMS) in Higher Education Institutions (HEIs) have become inevitable for teaching and learning process. It facilitates communications between teachers and students, as well as among students, regardless of physical barriers and time. However, there is also a dark side to the online environment, given more and more adolescents admit they have been the victims or bystanders of cyberbullying. In order to ensure that the online learning environment is safe for everyone, it is critical to assess the extent of the conversation carried out through the platform so as to ensure that no such misconduct is carried out via LMS. This paper also aimed to propose a practical solution to combat cyberbullying in an educational platform, and shed some lights on the potential applications of text mining techniques in educational research.

A domain knowledge incorporated text mining approach for capturing user needs on BIM applications

PurposeIn the architecture, engineering and construction (AEC) industry, technology developers have difficulties in fully understanding user needs due to the high domain knowledge threshold and the lack of effective and efficient methods to minimise information asymmetry between technology developers and AEC users. The paper aims to discuss this issue.Design/methodology/approachA synthetic approach combining domain knowledge and text mining techniques is proposed to help capture user needs, which is demonstrated using building information modelling (BIM) apps as a case. The synthetic approach includes the: collection and cleansing of BIM apps’ attribute data and users’ comments; incorporation of domain knowledge into the collected comments; performance of a sentiment analysis to distinguish positive and negative comments; exploration of the relationships between user sentiments and BIM apps’ attributes to unveil user preferences; and establishment of a topic model to identify problems frequently raised by users.FindingsThe results show that those BIM app categories with high user interest but low sentiments or supplies, such as “reality capture”, “interoperability” and “structural simulation and analysis”, should deserve greater efforts and attention from developers. BIM apps with continual updates and of small size are more preferred by users. Problems related to the “support for new Revit”, “import & export” and “external linkage” are most frequently complained by users.Originality/valueThe main contributions of this work include: the innovative application of text mining techniques to identify user needs to drive BIM apps development; and the development of a synthetic approach to orchestrating domain knowledge, text mining techniques (i.e. sentiment analysis and topic modelling) and statistical methods in order to help extract user needs for promoting the success of emerging technologies in the AEC industry.

Application of Text Mining Techniques for Evaluating Examination Question Paper

Application of Text Mining Techniques for Evaluating Examination Question Paper

Application of Text Mining Techniques in Railway Safety Supervision System

Railway safety is the most important in railway transportation. It is the cornerstone of all railway work and involves a wide range of departments. In the age of unstructured big data, it is an urgent problem to solve unstructured data, which contains a lot of information value by using advanced technology. This paper introduces the application of text analysis in railway safety supervision system based on massive data of accident and fault analysis report, analysis the importance of text mining techniques for railway safety system, the key technology of text analysis. Finally, based on the Railway Security Big Data Application Platform, we mainly introduce two classic functions of full-text retrieval and text classification based on text analysis technology on this platform. The application of the platform proves that text analysis plays a key role in railway safety supervision system

Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective

The emergence of online user-generated content has raised numerous questions about discourse gender differences as compared to face-to-face interactions. The intended gender-free equality of Internet has been challenged by numerous studies, and significant differences have been found in online communications. This paper proposes the application of text mining techniques to online gender discourse through the analysis of shared reviews in electronic word-of-mouth communities (eWOM), which is a form of user-generated content. More specifically, linguistic issues, sentiment analysis and content analysis were applied to online reviews from a gender perspective. The methodological approach includes gathering online reviews, pre-processing collected reviews and a statistical analysis of documents features to extract the differences between male and female discourses in a specific product category. Findings reveal not only the discourse differences between women and men but also their different preferences and the feasibility of predicting gender using a set of frequent key terms. These findings are interesting both for retailers so they can adapt their offer to the gender of customers, and for online recommender systems, as the proposed methodology can be used to predict the gender of users in those cases where the gender is not explicitly stated.

Reproducibility of studies on text mining for citation screening in systematic reviews: Evaluation and checklist

ContextIndependent validation of published scientific results through study replication is a pre-condition for accepting the validity of such results. In computation research, full replication is often unrealistic for independent results validation, therefore, study reproduction has been justified as the minimum acceptable standard to evaluate the validity of scientific claims. The application of text mining techniques to citation screening in the context of systematic literature reviews is a relatively young and growing computational field with high relevance for software engineering, medical research and other fields. However, there is little work so far on reproduction studies in the field. ObjectiveIn this paper, we investigate the reproducibility of studies in this area based on information contained in published articles and we propose reporting guidelines that could improve reproducibility. MethodsThe study was approached in two ways. Initially we attempted to reproduce results from six studies, which were based on the same raw dataset. Then, based on this experience, we identified steps considered essential to successful reproduction of text mining experiments and characterized them to measure how reproducible is a study given the information provided on these steps. 33 articles were systematically assessed for reproducibility using this approach. ResultsOur work revealed that it is currently difficult if not impossible to independently reproduce the results published in any of the studies investigated. The lack of information about the datasets used limits reproducibility of about 80% of the studies assessed. Also, information about the machine learning algorithms is inadequate in about 27% of the papers. On the plus side, the third party software tools used are mostly free and available. ConclusionsThe reproducibility potential of most of the studies can be significantly improved if more attention is paid to information provided on the datasets used, how they were partitioned and utilized, and how any randomization was controlled. We introduce a checklist of information that needs to be provided in order to ensure that a published study can be reproduced.