Text Mining Methods Research Articles

Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research

Background review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model.MethodsScanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy.Results The trained models’ micro f1-score for classifying abstracts, into three variables were 77.35% for COU, 76.24% for TS, and 85.64% for PRF.The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95% CI: 56.80–78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions (AAGR = 39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%).ConclusionsBioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews.

Effects of robots’ appearance on guest service experiences

ABSTRACT Robots are starting to replace human employees in the accommodation industry. It remains unclear how guests perceive robots. The study aims to evaluate the effectiveness of the humanoid and non-humanoid robots and to compare the guests’ perceptions. The data was collected by web digging and analyzed using text mining and machine learning. The results show that guests experience more positive emotions in non-humanoid robotic experiences. They also stated that they found both types of robot fun and interesting. However, guests have negative perceptions about the perceived usefulness and appearance of humanoid robots. According to the findings of the machine learning analysis, the neural network and logistic regression models show the best statistical performance in both datasets compared to other machine learning models. The study contributes to the literature through an investigation of guests’ opinions regarding robot types with the use of machine learning and text mining methods.

Promoting Sustainable Development in Urban–Rural Areas: A New Approach for Evaluating the Policies of Characteristic Towns in China

The Sustainable Development Goals, adopted by all United Nations Member States, emphasize sustainable cities and communities, aiming to strengthen development planning and foster positive links between urban and rural areas by 2030. As the newly leading platform for urbanization development, characteristic towns (‘Tese Xiaozhen’ in Chinese) contribute to the target of sustainable urbanisation, wherein town policies are especially important for leading the sound development of characteristic towns. However, the effect of these characteristic towns policies remains uncertain. Additionally, most related studies have utilized qualitative methodologies in policy evaluation, which may be inadequate to guide actual practice. Thus, in order to accurately acknowledge the effect of characteristic towns policies, a policy evaluation framework is established in this paper, utilising the multiple streams theory, text mining and the Policy Modelling Consistency Index model (PMC-Index model). Then, taking 225 policy documents promulgated in China from 2015 to 2022 as the research objects, policy evaluation indicators were selected by combining the multiple streams theory to improve the PMC-Index model and a text mining method. Thereafter, an empirical analysis was conducted to evaluate the consistency of 10 characteristic towns policies chosen. The results indicate that the mean value of the PMC indexes of the 10 examined policies is 7.13 in the total of 9 points, which means that the general performance of the characteristic town policies is satisfactory, yet the low scores in terms of effectiveness, content, and guarantee. In the end, some recommendations and suggestions are proposed for clarifying administrative attributes, optimizing the integration of urban and rural resources, and adjusting the policy hierarchy to a bottom-up approach. This study not only provides a policy evaluation framework to comprehensively understand the consistency of global urbanization policies but also offers a beneficial reference for promote urban–rural sustainable development.

Severity Analysis for Occupational Heat-related Injury Using the Multinomial Logit Model

BackgroundWorkers are often exposed to hazardous heat due to their work environment, leading to various injuries. As a result of climate change, heat-related injuries (HRIs) are becoming more problematic. This study aims to identify critical contributing factors to the severity of occupational HRIs. MethodsThis study analyzed historical injury reports from the Occupational Safety and Health Administration (OSHA). Contributing factors to the severity of HRIs were identified using text mining and model-free machine learning methods. The Multinomial Logit Model (MNL) was applied to explore the relationship between impact factors and the severity of HRIs. ResultsThe results indicated a higher risk of fatal HRIs among middle-aged, older, and male workers, particularly in the construction, service, manufacturing, and agriculture industries. In addition, a higher heat index, collapses, heart attacks, and fall accidents increased the severity of HRIs, while symptoms such as dehydration, dizziness, cramps, faintness, and vomiting reduced the likelihood of fatal HRIs. ConclusionsThe severity of HRIs was significantly influenced by factors like workers’ age, gender, industry type, heat index , symptoms, and secondary injuries. The findings underscore the need for tailored preventive strategies and training across different worker groups to mitigate HRIs risks.

A systematic evaluation of text mining methods for short texts: Mapping individuals’ internal states from online posts

Short texts generated by individuals in online environments can provide social and behavioral scientists with rich insights into these individuals’ internal states. Trained manual coders can reliably interpret expressions of such internal states in text. However, manual coding imposes restrictions on the number of texts that can be analyzed, limiting our ability to extract insights from large-scale textual data. We evaluate the performance of several automatic text analysis methods in approximating trained human coders’ evaluations across four coding tasks encompassing expressions of motives, norms, emotions, and stances. Our findings suggest that commonly used dictionaries, although performing well in identifying infrequent categories, generate false positives too frequently compared to other methods. We show that large language models trained on manually coded data yield the highest performance across all case studies. However, there are also instances where simpler methods show almost equal performance. Additionally, we evaluate the effectiveness of cutting-edge generative language models like GPT-4 in coding texts for internal states with the help of short instructions (so-called zero-shot classification). While promising, these models fall short of the performance of models trained on manually analyzed data. We discuss the strengths and weaknesses of various models and explore the trade-offs between model complexity and performance in different applications. Our work informs social and behavioral scientists of the challenges associated with text mining of large textual datasets, while providing best-practice recommendations.

Clustering Analysis of Hotel Network Reviews Based on Text Mining Method

With the development of information technology, users use online platforms to post real-time online comments to express their preferences and opinions on goods or services. Online review information expresses users' behavioral habits and special preferences. In depth, analysis of hotel online reviews can improve the adaptability of hotel services to user needs. Effective mining of the vast user review data will provide value for the development of the tourism industry. Using text mining methods to process hotel review data, multiple clustering methods were compared and analyzed for positive and negative feature words from the perspective of user experience. It was found that the k-means++ algorithm had a better clustering effect on user network reviews and achieved better clustering and segmentation of user evaluation information. Unsupervised clustering analysis can be used to further classify online comment information into categories based on positive and negative reviews, providing intellectual support for improving the precision and personalized service quality of hotels.

텍스트 마이닝을 이용한 코로나19 전후 청소년과 식생활 관련 키워드 검색 경향 분석

This study analyzed Naver, Daum, Google, YouTube, and Twitter using TEXTOM for two years and four years as of January 18, 2020. The results are as follows. First, the total number and volume of keyword search data related to youth and diet were slightly higher after COVID-19, showing that interest increased due to COVID-19. Second, as a result of frequency analysis, ‘education’ was the highest before COVID-19, and ‘health’ was the highest after COVID-19, showing that interest in health is increasing due to the increased importance of health and immunity due to COVID-19. Third, as a result of frequency weight analysis of the top 50 keywords, ‘education’ showed the highest frequency before COVID-19, and ‘acne’ after COVID-19. Fourth, the results visualized using word cloud showed that the keywords ‘education’ before COVID-19 and ‘health’ after COVID-19 appeared the largest and boldest, showing the highest frequency and importance. As a result of the above results, we were able to use the text mining method to apply it to eating habits, and we used materials visualized as a highly readable word cloud in units such as eating problems in adolescence and balanced meal planning and selection in the home economics curriculum to improve the teaching of the class. The direction of proper eating habits education, including using it as a medium, was presented.

Robotic Solutions for the Challenges of Human Labor: Managers Perspective

Managers have to endure some costs in order to develop human resources, which is one of the most significant providers of organizational performance. These costs are expenditures for human resources activities and customer dissatisfaction caused by work accidents and negative personnel behaviors. This study primarily aims to determine the expenditures made by the managers and the behavior of the employees that disturb the guests. Text mining methods is used to identify cost of human labor and employee behaviors referred to by managers' reviews, and association rules is used to find out common cost and behaviors in managers' reviews. The secondary aim of the study is to investigate the managers' intentions to employ robots to solve human-induced challenges. Contrary to the literature, managers believe that robots cannot contribute to the solution of these problems and they don’t think to work with service robots.

Towards a ‘synergy’ of text mining and critical discourse analysis: a corpus-assisted discourse study of imagining China in Hong Kong political discourse

Abstract This study proposes to incorporate text mining into critical discourse analysis (CDA) to give a corpus-assisted discourse study of the particular ways of imagining Hong Kong’s relations to China in the public speeches of three former Chief Executives in the two decades after its handover. With the computer-assisted text-mining tool KH Coder, this study combines the methods of quantitative text mining and qualitative discourse analysis to examine their preferential ways of imagining China at different levels of discourse: (1) topics/themes, (2) discursive strategies, and (3) linguistic means and realizations. It generates illuminating findings concerning their preferential ways of imagining Hong Kong’s relations to China as well as the analytic potential of incorporating text mining into CDA. It is expected that it can lead to more studies towards this endeavour.

Can local fintech development improve analysts’ earnings forecast accuracy? Evidence from China

This paper examines the impact of local fintech development on the accuracy of analysts’ earnings forecasts. We employ the method of web text mining to construct the local fintech development index for empirical test. Our findings indicate that local fintech development has a substantial positive impact on analysts’ earnings forecast accuracy by promoting firm digital transformation and alleviating the information asymmetry between firms and outsiders. Additionally, this effect is particularly pronounced for analysts without equity pledge affiliation and those with weaker buy-side pressure. This study shows that local fintech development can enhance the information environment of the capital market.

Sentiment Analysis of Indonesian Youtube Reviews About Lesbian, Guy, Bisexual and Transgender (LGBT) using IndoBERT Fine Tuning

Sentiment Analysis of Indonesian Youtube Reviews About Lesbian, Guy, Bisexual and Transgender (LGBT) using IndoBERT Fine Tuning

Text Mining technologies in sociological analysis (using the example of studying students`ideas about the mission of a modern university)

There are discussed in the article the possibilities of using Text Mining methods in the practice of analyzing the information received on the base of open questionnaire questions. The paper presents an example of unigrams and bigrams analysis, as well as the search for latent topic using thematic modeling. Empirical materials present the data of survey conducted in 2022, in which 929 students of one Moscow economics university took part. In the open question of the questionnaire, it was proposed to define the mission of the university. Information made it possible to get the subjective interpretation of the main significancy of higher education in modern conditions. The frequency analysis of unigrams, supplemented by a qualitative analysis of respondents’ statements, allowed reflecting the vocabulary of student discourse about the mission of the university. The articulation of bigrams was carried out on the basis of several statistical metrics, which made it possible to rank phrases and highlight a key set of concepts. The procedure revealed that in the perception of students, the priorities of the university are aimed at the transferring of professional knowledge and skills, in a broad sense – the training of qualified specialists. The social functions of the university, focused on meeting the needs of society and the state, are less pronounced in the conceptual interpretations of the interviewed students. At the next stage of the study the task of articulation and research of latent topics was put forward. The specific feature of thematic modeling is that the words combined into one topic reflect the distribution of words identified by the program, but not a topic that is literally understandable to a person. Taking into account the specifics of the method used, the authors demonstrated the results of search analysis in the practice of processing an open question. As it turned out, the keywords concentrated in the core of the main topics are mainly related to meeting the needs of the students themselves, leaving on the periphery of the verbalized definitions any understanding of the importance of the university as a platform for innovation, scientific research, entrepreneurial and other initiatives for the benefit of society and the country. The results of the presented research can be useful in rethinking the research tools of sociologists in the context of the active development of digital technologies, which requires testing new methods, understanding their real capabilities and limitations in solving the tasks of sociological research.

INTRICATE GENEALOGIES: WHAT IS SAID IN THE EPIC POEM ABOUT NOGAY PEOPLE?

This article examines communication blueprints of the Forty Knights of Steppe epic poem (original title in Kazakh "Қырымның қырық батыры"). The Forty Knights of Steppe is a cultural heritage of Turkic nations. The epic poem’s text is an assemblage of ballads that are connected with each other. It consists of thirty-five ballads and depicts relationships of around two hundred characters. The epic poem's events have a historical origin that links to the Nogay nobility from the Golden Horde period. The main event in the epic poem develops around Edige and his descendants. The historical aspect of this epic poem positions it with the paragons of heroic literature such as "Shahnameh" and "Manas". The epic poem’s text was first fully recorded in 1942 in Almaty by Muryn Zhyrau (real name ‘Tilegen Sengirbekuly’) by specialists from the Kazakh SSR Academy of Sciences. Unfortunately, the epic poem stayed unpublished for more than 60 years. The full text of the epic poem was published in 2005 after restoring the independence of the Republic of Kazakhstan. This research discovers the epic poem by analyzing their network. It focuses on mapping and visualizing links between characters described in the text. The authors used the methods of text mining, proposition, and semantic triangles for assembling information about contacts between characters into the databases. The databases include basic information, such as the names of characters and their contacts with other characters. The databases also contain information about the direction of every contact, relationship statuses of contactors, contactors' roles inside of genealogical lineage, kinship degrees of contactors inside their genealogies, and contactors' gender. In addition, the databases include information about the connection of contactors to genealogies, and information about the contacts of contactors outside of their genealogies. All databases were built in spatial data analysis compatible format. The databases were launched through data visualization software by switching on the environmental settings required for this type of research. The authors used Gephi data visualization software for this research to visualize databases. The visualization shows an intricate communicative network that covers almost all characters. The visualization demonstrates two types of contacts between characters of the epic poem. The first type is a structured type of contacts. This type of contacts develops inside of genealogies. It is a common type of contacts in the epic poem, and it follows the hierarchical order. The second type is a class-based type of contacts. This type of contacts ties characters from different genealogies and does not correspond to the hierarchical order. However, this type of contacts depends on the characters’ roles and statuses inside their genealogies. Thereby, the second type of contacts is connecting different genealogies into whole network. The types of contacts demonstrate two levels of social communication in the narration of the epic poem. Considering the visualization results and according to the historical origins of the characters, the authors argue that the text of the epic poem shows the patterns of social communication typical for Central Eurasian medieval nomad cultures in the Golden Horde period. On the other side, the stratification into two types of contacts may demonstrate the levels of bureaucracy in social communication in the same historical period. The authors suggest that the social networks of the epic poem describe the transitional form of the chiefdom society with communication levels typical for tribal and chiefdom societies. In general, the paper's authors suppose that the example of this research on the epic poem’s communication structure may give more data to understand the correlation between language and society.

Research on Barrage Text Mining and Emotional Orientation in Online Interactive Learning Videos: Based on the Case Study of "Luo Xiang's Criminal Law"

This study uses text mining and sentiment analysis methods to analyze the barrage text information of "Luo Xiang's Criminal Law" online teaching videos and sort out the main emotional tendencies of users. The research found that user emotions change with the dispersion of hot topics, showing a decentralized characteristic, and at the same time, users still have a certain lack of capture for the content of online teaching videos in the interactive learning process. It is urgent to clarify the specific impact of technology on users. This study is helpful for deepening research on online interactive learning, clarifying user emotional tendencies, and providing a decision-making basis for accurately improving interactive learning effects.

Application of a text mining method in navigation and communication for enhancing maritime safety.

This paper introduces a model for the translation of natural language into ontology and vice versa in an autonomous navigation system of a sea-going vessel. The system comprehensively executes communication tasks at sea. The authors use machine learning methods in the field of text mining and basic and additional properties of ontologies. The newly developed ontology is applicable in shipping. The key elements of the prototype are the sequence of communication commands given from the ship's bridge, decomposition, extraction of the communication sequence and the rule base. The presented model has been implemented and verified in selected scenarios of collision situations at sea. The test results confirm that the assumptions, the designed system architecture and the algorithms in the prototype are correct.

Assessing the sustainability of the European Green Deal and its interlin kages with the SDGs

The European Green Deal (EGD) is the growth strategy for Europe, covering multiple domains, and aiming to an equitable, climate neutral European Union by 2050. The UN Agenda 2030, encompassing 17 Sustainable Development Goals (SDGs), establishes the foundation for a global sustainability transition. The integration of the SDGs into the EGD is an overlooked issue in the literature, despite Europe’s slow progress to achieve the sustainability targets. We employed a machine-learning text-mining method to evaluate the extent of SDG integration within the 74 EGD policy documents published during 2019–2023. The findings reveal a substantial alignment of EGD policies with SDGs related to clean energy (SDG7), climate action (SDG13), and sustainable consumption and production (SDG12). In contrast, there is a significant underrepresentation in areas related to social issues such as inequalities, poverty, hunger, health, education, gender equality, decent work, and peace, as indicated by lower alignment with SDGs 1, 2, 3, 4, 5, 8, 10, and 16. Temporal trends suggest a marginal increase in the attention given to environmental health (especially water and marine life) and gender equality. Furthermore, we illustrate the alignment of EGD policies with the six essential sustainability transformations proposed by the Sustainable Development Solutions Network (SDSN) in 2019 for the operationalization of the SDGs. The results indicate that besides the prevalence of “Energy Decarbonization and Sustainable Industry”, all areas have received attention, except for the “Health, Wellbeing and Demography”. The findings call for a more integrated approach to address the complete spectrum of sustainability in a balanced manner.

Trend mining with Orange – using topic modeling in futures research with the example of urban mobility

Today, assumptions about probable future developments (at least as far as they make use of quantifiable scientific methods and are not pure speculation) are generally based on data from the past. An interesting way to analyze the future through this type of data is text mining or individual methods out of the spectrum of text mining, such as topic modeling. Topic Modeling itself is a combination of quantitative and qualitative methodology and is based on the full spectrum of social science methodology. Therefore, the method is an interesting way for futures research to analyze futures. This publication addresses the question of how a combination of different methods can contribute to trend monitoring or trend mining. For this purpose, a set of scientific publications was first generated with the help of a search query in the Web of Science (WoS), which is the basis for all evaluations and statements and topics. In essence, the method considered here should be more fully integrated into the scientific practice of futures research because it can make a valuable contribution to estimating future development based on past development.

Comparison of Impressions of COVID-19 Vaccinations Stratified by the Number of Vaccinations Among Japanese Healthcare Professional University Students.

Coronavirus infectious disease, that emerged in 2019 (COVID-19) has been a major public health issue not only in Japan, but also worldwide, and the implementation of a proper vaccination strategy has been important. To promote vaccination, the present study compared impressions of COVID-19 vaccinations stratified by the number of vaccinations among healthcare professional university students in Okayama, Japan, and suggests better vaccination strategies. A total of 212 Japanese healthcare professional university students were enrolled in this clinical qualitative study using the text mining method. A self-reported questionnaire, including questions such as "What do you think about COVID-19 vaccinations?"was performed. We also examined the number of vaccinations, sex, history of COVID-19 infection, and daily mask use. A total of 5,935 words were obtained and "Think" (169 times) was the most frequently used followed by "Inject" (108 times), "Inoculation" (97 times), "Vaccine" (83 times), "Corona" (66 times) and "Side effects" (49 times). Characteristic words were "Safety" in non-vaccinated subjects and "Side effects" and "Necessary" in vaccinated subjects. In addition, "Safety" in non-vaccinated men and "Frightening" in non-vaccinated women were characteristic and fundamental features. Impressions of COVID-19 vaccinations stratified by the number of vaccinations differed among healthcare professional university students. The provision of appropriate information on safety to non-vaccinated subjects and side effects to vaccinated subjects appears to be necessary. In addition, sex-specific information may be required for non-vaccinated subjects.

Mapping Ethical Artificial Intelligence Policy Landscape: A Mixed Method Analysis

As more national governments adopt policies addressing the ethical implications of artificial intelligence, a comparative analysis of policy documents on these topics can provide valuable insights into emerging concerns and areas of shared importance. This study critically examines 57 policy documents pertaining to ethical AI originating from 24 distinct countries, employing a combination of computational text mining methods and qualitative content analysis. The primary objective is to methodically identify common themes throughout these policy documents and perform a comparative analysis of the ways in which various governments give priority to crucial matters. A total of nineteen topics were initially retrieved. Through an iterative coding process, six overarching themes were identified: principles, the protection of personal data, governmental roles and responsibilities, procedural guidelines, governance and monitoring mechanisms, and epistemological considerations. Furthermore, the research revealed 31 ethical dilemmas pertaining to AI that had been overlooked previously but are now emerging. These dilemmas have been referred to in different extents throughout the policy documents. This research makes a scholarly contribution to the expanding field of technology policy formulations at the national level by analyzing similarities and differences among countries. Furthermore, this analysis has practical ramifications for policymakers who are attempting to comprehend prevailing trends and potentially neglected domains that demand focus in the ever-evolving field of artificial intelligence.

Unlocking the Potential of Digital Twins in Construction: A Systematic and Quantitative Review Using Text Mining

The construction industry has been trying to enhance the level of digitalization and autonomy by adopting various communication and information technologies (ICT), e.g., augmented reality (AR), virtual reality (VR), robotics, drones, or building information modeling (BIM). However, improvement of the safety and productivity in their domains is still a struggle. One of the main reasons for failing to accelerate their digital transformation is ignoring the deep understanding of the concept of digital twin, its usage, and the potential benefits of digital twins in the construction industry. Therefore, this paper investigated the impacts and potentials of digital twins on the construction industry through a quantitative systematic review assisted by the text mining method. The study presented the potential usability of digital twins, leading and core technologies, and applications, revealing their benefits and potential for optimizing project planning, execution, and management process. Through this comprehensive literature review, this study elucidated the distinctive features, advantages, and immense potential that digital twins bring to the construction field. The findings highlight the transformative impact of digital twins, providing critical insights for their broader adoption and groundbreaking applications in the industry. By addressing the challenges of adopting this technology, the article provided valuable insights for advancing research and the broad implementation of digital twins in the sector.