Applying Text Mining Techniques Research Articles

텍스트 마이닝을 활용한 국내 기본심리욕구 연구 동향 분석

Objectives The purpose of this study is to examine research trends related to basic psychological needs in Korea and identify major research topics and trends. Methods To this end, we analyzed 327 academic papers related to basic psychological needs, which were more than candidates for KCI registration published on the Academic Research Information Service. The data was preprocessed through an R program, and the results of keyword frequency analysis were visualized in a word cloud, and analyzed by topic modeling. Results First, according to the literature status, 327 journals have been published in 106 societies, including the Learner-Centered Curriculum Education Association and the Korean Youth Association. Second, in keyword frequency analysis, terms such as basic psychology, desire, research, relationship, influence, mediation, analysis, autonomy, effectiveness, satisfaction, and competence appeared as top words. Third, topic modeling results were categorized by effects on individual psychological well-being, research methods, relationships with elementary school students and psychological well-being, the influence of young people's home and school environments, the mental health of college students, and relationships with stressors in modern society. Conclusions This study is significant in that it identified the characteristics of basic psychological needs research trends in Korea by applying text mining techniques to a vast amount of information on basic psychological needs, and it can be used as useful basic data for setting the direction of basic psychological needs research and selecting research topics in the future.

Analysis of child development facts and myths using text mining techniques and classification models

The rapid dissemination of misinformation on the internet complicates the decision-making process for individuals seeking reliable information, particularly parents researching child development topics. This misinformation can lead to adverse consequences, such as inappropriate treatment of children based on myths. While previous research has utilized text-mining techniques to predict child abuse cases, there has been a gap in the analysis of child development myths and facts. This study addresses this gap by applying text mining techniques and classification models to distinguish between myths and facts about child development, leveraging newly gathered data from publicly available websites. The research methodology involved several stages. First, text mining techniques were employed to pre-process the data, ensuring enhanced accuracy. Subsequently, the structured data was analysed using six robust Machine Learning (ML) classifiers and one Deep Learning (DL) model, with two feature extraction techniques applied to assess their performance across three different training-testing splits. To ensure the reliability of the results, cross-validation was performed using both k-fold and leave-one-out methods. Among the classification models tested, Logistic Regression (LR) demonstrated the highest accuracy, achieving a 90 % accuracy with the Bag-of-Words (BoW) feature extraction technique. LR stands out for its exceptional speed and efficiency, maintaining low testing time per statement (0.97 μs). These findings suggest that LR, when combined with BoW, is effective in accurately classifying child development information, thus providing a valuable tool for combating misinformation and assisting parents in making informed decisions.

Text Value and Linguistic Characterization in Chinese Language Literature Based on Text Mining Techniques

Abstract This study applies text mining techniques to deeply analyze Chinese language and literature’s text value and linguistic features. The study adopts the methods of textual disambiguation, vector space modeling, semantic network and Labeled-LDA model. Taking the novels of Yu Hua and Ge Fei as an example, it reveals the differences between the two writers in linguistic features such as using punctuation, average word length, and sentence discrete degree. The study provides a comprehensive heat score for the novels based on three dimensions: reading base group, reading gain, and reading discussion. The results show that the frequency of period use in Yu Hua’s works is decentralized, while Ge Fei’s works are more concentrated. Ge Fei’s average word length is slightly higher, showing a tendency to use multi-syllabic words. The novel popularity and heat scores conform to a power law distribution, reflecting the Pareto rule that 80% of the popularity is concentrated on 20% of the hot novels. This study provides a new perspective on Chinese language and literature through the application of text mining technology, and its methods and tools can effectively enhance the effectiveness and efficiency of teaching.

Evaluating Road Hazard Maintenance Efficiency Using Citizen Science Data to Improve Road Safety

Accidents caused by road hazards can be prevented through regular inspections by road management agencies. To this end, traffic agencies allocate substantial budgets and workforces to maintain the performance of roads. Additionally, traffic agencies require comprehensive data such as the classifications and sizes of road hazards. However, collecting spatial–temporal data on various road hazards is challenging, and evaluating it comprehensively with respect to work efficiency and budget allocation is difficult due to stakeholder interests across agencies. This study proposes a process of evaluating operational efficiency in terms of maintaining roads and preventing hazards by analyzing citizen scientist-based data. First, we collected data from drivers through a mobile application and applied text mining techniques to classify each complaint into several types of road hazard maintenance. Second, we developed an indicator to measure operational efficiency using the processed data and evaluated each regional agency per each type of maintenance. The results of this study provide evidence that specific types of road hazards occur prominently under specific agencies. In addition, the time required to provide maintenance for identical road hazards can vary among agencies. These results suggest that the maintenance budget for the entire national highway may need to be distributed differently based upon regional characteristics.

Hot topic detection in central bankers’ speeches

Analysis of central bankers’ statements is essential to understand current economic conditions and predict future market trends. The most popular method used for this purpose – qualitative monitoring and evaluation (QME) – is based on the input of domain experts, which is expensive and subjective by nature. Several researchers have attempted to use text mining techniques as alternatives. However, they have primarily focused on aligning textual information with macroeconomic indicators, rather than directly extracting keywords. The primary aim of this study is to identify the possibility of automatically detecting potential financial risk factors by applying text mining techniques to central bankers’ speeches. We propose a text mining framework to extract risk factors ex ante by detecting hot topics in speeches made by chairs of the Federal Reserve System. In the framework, we use a simple and effective unsupervised keyword-scoring method,11The source code used for this study is available at https://github.com/sophia-jihye/hot_topic_detection_in_central_bankers_speeches. which treats bigrams as keywords and incorporates the temporal importance of keywords by estimating the “emergence” of a term at a certain time period relative to previous time periods. In-depth analysis through extensive experiments was conducted to compare risk factor detection performance using eight existing methods Including statistical approaches and recent pretrained language model-based approaches. Experimental results demonstrate that our proposed method adopting a statistical approach effectively captures potential risk factors in central bankers’ speeches. Application of the proposed framework to manuscripts of speeches made between 1997 and 201922The dataset collected for this study is available at https://github.com/sophia-jihye/bis_speeches_text_dataset. revealed that the recurrence of the terms Such as “east asia” “information technology” and “subprime mortgage” which describe the Asian financial crisis Dot-com bubble And global financial crisis Respectively Could have been detected prior to the onset of the respective crises. Furthermore The quantitative evaluation revealed that the frequency of appearance of the phrase “subprime mortgage” increased by more than 400% in speeches made during the second quarter of 2007 as compared to the previous quarter Whereas the number of the New York times news articles containing the word “subprime mortgage” only increased by approximately 0%. our results show that central bankers’ speeches can be used as an appropriate data source for the ex ante identification of market risk factors. We anticipate that this study will contribute to broadening the understanding of trends in hot topics in the financial market Monitoring potential risk factors And developing preemptive response plans to deal with impending financial crises.

Analysis of Crisis Leadership Research Trends Using Text Mining: Focusing on Business Administration

This study was to analyze research trends in the field of business administration by applying text mining techniques. In particular, the 2008 global financial crisis caused many management difficulties for companies. It was intended to consider how these events were reflected in research trends. This study performed 'frequency analysis', 'TF-IDF analysis', and 'topic analysis' among the text mining techniques. First, before and after the global crisis, “management” and “leadership” were ranked top tier. Also 'leadership' and 'management' belonged to the top tier in the total period. Second, the most noticeable word was 'safety' before 2008. And 'integration' is the top priority after 2008. Thus, it was found that the business administration field dealt with 'integration' and 'decision making' meaningfully after 2008. Third, we found a paper that corresponds to the gamma value by topics. Key words explain these papers well. In this way, the meaning of extracted words was analyzed by applying text mining techniques, and research trends in the field of business administration were examined through this. In addition, words that appear important and words with special meanings were analyzed by comparing around 2008.

An Informed Neural Network for Discovering Historical Documentation Assisting the Repatriation of Indigenous Ancestral Human Remains

Among the pressing issues facing Australian and other First Nations peoples is the repatriation of the bodily remains of their ancestors, which are currently held in Western scientific institutions. The success of securing the return of these remains to their communities for reburial depends largely on locating information within scientific and other literature published between 1790 and 1970 documenting their theft, donation, sale, or exchange between institutions. This article reports on collaborative research by data scientists and social science researchers in the Research, Reconcile, Renew Network (RRR) to develop and apply text mining techniques to identify this vital information. We describe our work to date on developing a machine learning-based solution to automate the process of finding and semantically analysing relevant texts. Classification models, particularly deep learning-based models, are known to have low accuracy when trained with small amounts of labelled (i.e. relevant/non-relevant) documents. To improve the accuracy of our detection model, we explore the use of an Informed Neural Network (INN) model that describes documentary content using expert-informed contextual knowledge. Only a few labelled documents are used to provide specificity to the model, using conceptually related keywords identified by RRR experts in provenance research. The results confirm the value of using an INN network model for identifying relevant documents related to the investigation of the global commercial trade in Indigenous human remains. Empirical analysis suggests that this INN model can be generalized for use by other researchers in the social sciences and humanities who want to extract relevant information from large textual corpora.

Enhancing the government accounting information systems using social media information: An application of text mining and machine learning

This study demonstrates a way of bringing an innovative data source, social media information, to the government accounting information systems to support accountability to stakeholders and managerial decision-making. Future accounting and auditing processes will heavily rely on multiple forms of exogenous data. As an example of the techniques that could be used to generate this needed information, the study applies text mining techniques and machine learning algorithms to Twitter data. The information is developed as an alternative performance measure for NYC street cleanliness. It utilizes Naïve Bayes, Random Forest, and XGBoost to classify the tweets, illustrates how to use the sampling method to solve the imbalanced class distribution issue, and uses VADER sentiment to derive the public opinion about street cleanliness. This study also extends the research to another social media platform, Facebook, and finds that the incremental value is different between the two social media platforms. This data can then be linked to government accounting information systems to evaluate costs and provide a better understanding of the efficiency and effectiveness of operations.

Can a wonder material be a popular item? A hype cycle of shifts in the sentiment of the interested public about graphene

ABSTRACT Although graphene has created a strong wave of enthusiasm after its discovery in 2004 and the subsequent Nobel prize awarded to the two detectors in 2010, there is no clear information how people with special interests in it feel toward the possibility of its commercial value over time. The objective of this study is to identify graphene sentiment shift and major discussion topics among the interested public on Reddit by presenting an effective tool for extracting public opinions in social media. To achieve this, we processed 11,287 comments collected from the Reddit from 2010 to 2021, and applied text mining techniques, including sentiment analysis. This study found that over half of the comments were positive toward graphene, and specific sentiment fluctuated commensurate with major events, for example, Nobel Prize, commercialisation delay and wide applications to the industry. Our research proved that public sentiments toward graphene technology follow a similar trend to the hype cycle by deriving a sentiment hype cycle for graphene. This study provides a convincing case for understanding shifts in the sentiments of the interested public regarding new technology, such as graphene, which will interest graphene technology R&D experts and policy-makers.

Exploring the Applicability of Topic Modeling in SARS-CoV-2 Literature and Impact on Agriculture

For the last two years, countries around the globe have been suff ering and severely aff ected by the Covid-19 pandemic due to the novel coronavirus. Researchers from various disciplines are conducting research and publishing number of articles related to this virus and its eff ects. Furthermore, articles related to Covid-19 are being continuously published in the form of research papers, popular articles, blogs, surveys, short stories etc. These possess useful information and this information can be processed to infer important knowledge by applying text mining techniques. The Latent Dirichlet Allocation (LDA) technique provides an effi cient way to analyse unclassifi ed text into useful sets of terms, called topics. LDA can group terms with similar semantic meaning into topics called "themes". A theme is a group of terms that frequently appear together. The objective of the present study is to explore the applicability of topic modeling in identifying the hidden themes or topics by using published research articles related to Covid-19 and agriculture through Google scholar. After pre-processing of titles and abstracts, two approaches namely LDA with Bag of Words (LDAB) and LDA with Term Frequency-Inverse Document Frequency (LDAT) were applied to fi nd the hidden themes. There are thirteen and seven topics are identifi ed by applying LDAB and LDAT respectively. These identifi ed topics comprised with diff erent set of words or features will play an important role in developing the information retrieval system for specifi c search related to agricultural production, supply chain mechanism in agriculture, health and agri-tourism.

Identifying kidney trade networks using web scraping data

Kidney trade has been on the rise despite the domestic and international law enforcement aiming to protect the vulnerable population from potential exploitation. Regional hubs are emerging in several parts...

Toward User-Generated Content as a Mechanism of Digital Placemaking—Place Experience Dimensions in Spatial Media

Spatial media bring out new forms of interaction with places, leading to the emergence of new ways of embodying the experience. The perception of place and its dynamics of change has been multiplied by the emergence of digital platforms, which create many and varied representations of place in spatial media. These representations are dependent on the digital platforms’ ecosystem, formed by platform-specific mechanisms of digital placemaking. The study applied text mining techniques and statistical methods to explore the role of user-generated content as a digital placemaking practice in shaping place experience. The online reviews were collected from Google Maps for 23 places from Poznań, Poland. The analysis showed that place experience is described by three dimensions: attributes, practices and atmosphere, or place practices that most closely reflect the specificity of a place. The place attributes blurred the boundaries between their digital images, whereas the atmosphere dimension reduces the diversity and uniqueness of the place. In conclusion, user-generated content (UGC) as an element of the process of digital placemaking increases place awareness and democratizes human participation in its creation, yet it affects its reduction to homogeneous information processed through mechanisms operating within a given digital platform.

Examining the Communication Pattern of Transportation and Transit Agencies on Twitter: A Longitudinal Study in the Emergence of COVID-19 on Twitter

Social media can be a significant tool for transportation and transit agencies providing passengers with real-time information on traffic events. Moreover, COVID-19 and other limitations have compelled the agencies to engage with travelers online to promote public knowledge about COVID-related issues. It is, therefore, important to understand the agencies’ communication patterns. In this original study, the Twitter communication patterns of different transportation actors—types of message, communication sufficiency, consistency, and coordination—were examined using a social media data-driven approach applying text mining techniques and dynamic network analysis. A total of 850,000 tweets from 395 different transportation and transit agencies, starting in 2018 and the periods before, during and after the pandemic, were studied. Transportation agencies (federal, state, and city) were found to be less active on Twitter and mostly discussed safety measures, project management, and so forth. By contrast, the transit agencies (local bus and light, heavy, and commuter rail) were more active on Twitter and shared information about crashes, schedule information, passenger services, and so forth. Moreover, transportation agencies shared minimal pandemic safety information than transit agencies. Dynamic network analysis reveals interaction patterns among different transportation actors that are poorly connected and coordinated among themselves and with different health agencies (e.g., Centers for Disease Control and Prevention [CDC] and the Federal Emergency Management Agency [FEMA]). The outcome of this study provides understanding to improve existing communication plans, critical information dissemination efficacy, and the coordination of different transportation actors in general and during unprecedented health crises.

State of Industry 5.0—Analysis and Identification of Current Research Trends

The term Industry 4.0, coined to be the fourth industrial revolution, refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry. With Industry 4.0 being unable to address and meet increased drive of personalization, the term Industry 5.0 was coined for addressing personalized manufacturing and empowering humans in manufacturing processes. The onset of the term Industry 5.0 is observed to have various views of how it is defined and what constitutes the reconciliation between humans and machines. This serves as the motivation of this paper in identifying and analyzing the various themes and research trends of what Industry 5.0 is using text mining tools and techniques. Toward this, the abstracts of 196 published papers based on the keyword “Industry 5.0” search in IEEE, science direct and MDPI data bases were extracted. Data cleaning and preprocessing were performed for further analysis to apply text mining techniques of key terms extraction and frequency analysis. Further topic mining i.e., unsupervised machine learning method was used for exploring the data. It is observed that the terms artificial intelligence (AI), big data, supply chain, digital transformation, machine learning, internet of things (IoT), are among the most often used and among several enablers that have been identified by researchers to drive Industry 5.0. Five major themes of Industry 5.0 addressing, supply chain evaluation and optimization, enterprise innovation and digitization, smart and sustainable manufacturing, transformation driven by IoT, AI, and Big Data, and Human-machine connectivity were classified among the published literature, highlighting the research themes that can be further explored. It is observed that the theme of Industry 5.0 as a gateway towards human machine connectivity and co-existence is gaining more interest among the research community in the recent years.

Five decades lead in biomedical research: University of Khartoum tops Sudanese universities in PubMed articles

Introduction: University of Khartoum (U of K) is the largest and oldest university in Sudan (1). The recent rankings of international and local universities made by Webometrics Ranking of world Universities initiative in 2017 have placed University of Khartoum as number one University in Sudan and 1970 in rank Worldwide. This ranking system is based mainly on research activity and publications. Using PubMed we obtained data on all PubMed-indexed articles (from 1966 to 2016) affiliated to the four oldest and most established Sudanese universities: University of Khartoum, University of Gezira, Omdurman Islamic University, and Al-Neelain University. Methods: Using R Statistical environment, we compiled a unified dataset from multiple resources including PubMed data and impact factor data. This unified dataset was then used to calculate multiple article and authorship metrics including total number of articles and annual averages. We also applied text mining techniques to from indexed abstracts in order to explore the prevailing trends and research focus of some the most prolific authors and to visualize this trends though word clouds. Results: Since the earliest date of PubMed indexing in 1966, the University of Khartoum is by far the most affiliated Sudanese university in PubMed. Approaching 300 articles in 2016, the number of PubMed indexed articles has shown a promising rising trend over the past decade. Our results clearly indicate lead of University of Khartoum in published biomedical research. This trend is promising and shows the attention shift towards research activities among the major Sudanese universities in general and University of Khartoum in particular.

데이터마이닝 기법을 활용한 관광목적지 이미지와 온라인 관광정보의 지각된 유용성과의 관계분석

This study seeks to analyze the relationship of tourism destination images and perceived usefulness of potential tourists in English reviews published on TripAdvisor, a post-travel reviews website belonging to online tourism information. The research method utilizes R programs, an open-source statistical software, to apply text mining techniques to quantify unstructured text data. First, a total of 25,016 reviews from 20 Seoul tourism destinations of TripAdvisor were collected with data mining techniques to extract the words, and a simple regression analysis was conducted with each extracted word. To test the hypothesis, a negative binary regression was conducted. The results of this study are as follows. First, it was confirmed that the tourism destination images affect perceived usefulness. Second, we were able to see the relationship among the tourism destination images. Finally, based on the results of these empirical analyses, we present academic and practical implications.

Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation: The case of gluten bibliome

The increasing number of scientific research documents published keeps growing at an unprecedented rate, making it increasingly difficult to access practical information within a target domain. This situation is motivating a growing interest in applying text mining techniques for the automatic processing of text resources to structure the information that helps researchers to find information of interest and infer knowledge of practical use. However, the automatic processing of research documents requires the previous existence of large, manually annotated text corpora to develop robust and accurate text mining processing methods and machine learning models. In this context, semi-automatic extraction techniques based on structured data and state-of-the-art biomedical tools appear to have significant potential to enhance curator productivity and reduce the costs of document curation. In this line, this work proposes a semi-automatic machine learning workflow and a NER + Ontology boosting technique for the automatic classification of biomedical literature. The practical relevance of the proposed approach has been proven in the curation of 4,115 gluten-related documents extracted from PubMed and contrasted against the word embedding alternative. Comparing the results of the experiments, the proposed NER + Ontology technique is an effective alternative to other state-of-the-art document representation techniques to process the existing biomedical literature.

Tracking Topics and Frames Regarding Sustainability Transformations during the Onset of the COVID-19 Crisis

Many researchers and politicians believe that the COVID-19 crisis may have opened a “window of opportunity” to spur sustainability transformations. Still, evidence for such a dynamic is currently lacking. Here, we propose the linkage of “big data” and “thick data” methods for monitoring debates on transformation processes by following the COVID-19 discourse on ecological sustainability in Germany. We analysed variations in the topics discussed by applying text mining techniques to a corpus with 84,500 newspaper articles published during the first COVID-19 wave. This allowed us to attain a unique and previously inaccessible “bird’s eye view” of how these topics evolved. To deepen our understanding of prominent frames, a qualitative content analysis was undertaken. Furthermore, we investigated public awareness by analysing online search behaviour. The findings show an underrepresentation of sustainability topics in the German news during the early stages of the crisis. Similarly, public awareness regarding climate change was found to be reduced. Nevertheless, by examining the newspaper data in detail, we found that the pandemic is often seen as a chance for sustainability transformations—but not without a set of challenges. Our mixed-methods approach enabled us to bridge knowledge gaps between qualitative and quantitative research by “thickening” and providing context to data-driven analyses. By monitoring whether or not the current crisis is seen as a chance for sustainability transformations, we provide insights for environmental policy in times of crisis.

Identification and analysis of misclassified work-zone crashes using text mining techniques

Work zone safety management and research relies heavily on the quality of work zone crash data. However, it is possible that a police officer may misclassify a crash in structured data due to: restrictive options in the crash report; a lack of understanding about their importance; lack of time due to police officers’ work load; and ignorance of work zone as one of the crash contributing factors. Consequently, work zone crashes are under representative in crash statistics. Crash narratives contain valuable information that is not included in the structured data. The objective of this study is to develop a classifier that applies text mining techniques to quickly find missed work zone (WZ) crashes through the unstructured text saved in the crash narratives.The study used three-year crash data from 2017 to 2019. The data from 2017 to 2018 was used as training data, and the 2019 data was used as testing data. A unigram + bigram noisy-OR classifier was developed and proven to be an efficient and effective means of classifying work zone crashes based on key information in the crash narrative. The ad-hoc analysis of misclassified work zone crashes sheds light on when, where and the plausible reasons as to why work zone crashes are more likely to be missed.

Identifying Emerging Trends in Scientific Texts Using TF-IDF Algorithm: A Case Study of Medical Librarianship and Information Articles

Background: Nowadays, due to the increased publication of articles in various scientific fields, identifying the publishing trends and emerging keywords in the texts of these articles is essential.
 Objectives: Thus, the present study identified and analyzed the keywords used in the published articles on medical librarianship and information.
 Methods: In the present investigation, an exploratory and descriptive approach was used to analyze librarianship and information articles published in specialized journals in this field from 1964 to 2019 by applying text mining techniques. The TF-IDF weighting algorithm was applied to identify the most important keywords used in the articles. The Python programming language was used to implement text mining algorithms.
 Results: The results obtained from the TF-IDF algorithm indicated that the words “Library”, “Patient”, and “Inform” with the weights of 95.087, 65.796, and 63.386, respectively, were the most important keywords in the published articles on medical librarianship and information. Also, the words “Catalog”, “Book”, and “Journal” were the most important keywords used in the articles published between the years 1960 and 1970, and the words “Patient”, “Bookstore”, and “Intervent” were the most important keywords used in articles on medical librarianship and information published from 2015 to 2020. The words “Blockchain”, “Telerehabilit”, “Instagram”, “WeChat”, and “Comic” were new keywords observed in articles on medical librarianship and information between 2015 and 2020.
 Conclusions: The results of the present study revealed that the keywords used in articles on medical librarianship and information were not consistent over time and have undergone a change at different periods so that nowadays, this field of science has also changed following the needs of society with the advent and growth of information technologies.