Content Mining Research Articles

Identifying Coping Strategies and Coping Resources Identifying Coping Strategies and Coping Resources COVID-19 Pandemic

Objectives: Prior research stresses the importance of effective coping strategies for positive adaptation, enhanced resilience, personal growth, and increased psychological health. Unfortunately, little research has investigated how Koreans with physical disabilities dealt with the COVID-19 pandemic. We aimed to identify coping strategies and resources that Koreans with physical disabilities applied during the COVID-19 pandemic. Methods: Using a purposeful criterion sampling strategy, we conducted semi-structured indepth interviews. A total of 15 participants (11 men and 4 women) who were between 22 and 65 years of age participated in the study. Content mapping and content mining questions were used. We analyzed data qualitatively using the constant comparative method to generate emergent themes. Results: To deal with various COVID-related challenges, we identified 3 salient themes used as coping strategies by participants: (1) engaging in meaningful activities, (2) improving social and emotional support, and (3) experiencing resilience. Conclusions: Our findings address qualitative evidence that Korean adults with spinal cord injury (SCI) actively engaged in various activities, maintained and developed closer relationships, and experienced resilience to deal with COVID-19 related stressors. These strategies suggest the importance of effective coping strategies and resources as a catalyst for health and wellbeing of people with SCI.

Driving forces and obstacles analysis of urban high-quality development in Chengdu.

High-quality development paths in important cities are blurry and lacking. In order to explore the important engine for Chengdu high-quality development, driving forces and obstacles recognition has emerged as a pivotal technological solution. Using the Chengdu in Sichuan province of China as a research area and quantitative data from 2010 to 2019, this study has used content mining to recognize urban high-quality development (UHQD) variables, and calculated variables' weights by entropy weight method, and explored driving forces and obstacles of UHQD by the technique for order preference by similarity to ideal solution (TOPSIS) method. The main findings are: (1) there are 36 UHQD variables; (2) Chengdu high-quality development overall level soars from 2017 to 2019, only with two negative growth rates in 2011, 2015; (3) There are 3 key driving force paths: (1) improving green development by volume of industrial wastewater discharged, comprehensively utilised ratio of industrial solid wastes, harmless treatment rate of domestic garbage; (2) stressing open development by total import and export/GDP, actual use of foreign capital, number of foreign tourists/total tourists; and (3) intensifying shared development by funds for urban residents under basic provision protection. (4) 3 clearing obstacles paths can also realize Chengdu high-quality development: (1) improving innovative development level by R&D internal outlay, patent authorisations, state high-level tech enterprises; (2) optimizing coordinated development level by the proportion of tertiary industry; (3) promoting shared development level by urban basic pension insurance. According to these findings, suggestions are put forward to promote Chengdu high-quality development from the perspective of policy implementation.

Topic Mining and Sentiment Analysis based on the Online Review from MOOC Platform’s Logistics Course

In the context of ongoing curriculum teaching reform accelerated by the COVID-19 pandemic, evaluating online logistics courses from the learners’ perspectives and identifying strategies for improvement have become critical areas of inquiry. Despite the growing body of literature on online education, existing studies often overlook the specific topics of interest and emotional tendencies of online logistics course learners. This study addresses this gap by utilizing the push-pull-mooring (PPM) theory, originally developed for human migration studies, to elucidate the transition of learners from offline to online environments, while exploring the formation process and significance of online logistics course instruction. We employ the ROST Content Mining System (ROST CM) software to conduct word frequency analysis, semantic network analysis, and sentiment analysis on review text data from two logistics courses offered on the China University massive open online courses (MOOC) platform. Our findings indicate that online learners prioritize three key aspects: “teacher,”“course,” and “learning content,” with high-frequency words revealing a hierarchical structure of “topic → transition → emotion.” While most learners expressed positive and satisfactory sentiments, a minority conveyed neutral or negative emotions. Based on these insights, this paper proposes targeted recommendations focusing on instructor guidance, enhancing online interaction, and fostering reflective teaching practices.

The transformation of 20-year social participation policies of older people in China: Network analysis and text analysis.

Social participation of older adults is a crucial component of China's aged care services and an important strategy for actively addressing the aging population. Analyzing policy texts on older people's social participation can inform future policy formulation and the development of relevant programs. This study aims to quantitatively analyze the transformation of China's social participation policies for older people from 1999 to 2023, employing institutional network analysis and policy text analysis. A two-dimensional policy analysis framework was constructed based on the perspective of "policy tools and social participation stages." Using Rost Content Mining 6.0 and Nvivo 11.0 Plus software, 55 national-level policy texts were coded. Structural analysis of policy-issuing subjects and topic words was conducted to visualize the findings. The analysis revealed that the policy-issuing subjects demonstrated strong authority but weak coordination, with a lack of communication and cooperation across subjects. The use of policy tools was imbalanced, with an over-reliance on supply-type tools and insufficient use of demand-type tools. Additionally, the lack of effective policy tools to support various social participation stages has limited policy implementation. With technological advancement and changing needs of the elderly population, there is a need for a more systematic and forward-looking top-level design of elderly social participation policies: accelerating the systematization and precision of technological elements in policies for elderly social participation, integrating social organizations via technological platforms to mobilize diverse stakeholder engagement, and addressing the digital divide between the elderly and new technologies is imperative.

Exploring the Synergy of Web Usage Data and Content Mining for Personalized Effectiveness

In light of the exponential growth of web data and user volume, individuals are increasingly overwhelmed by information overload on the internet. Addressing this challenge, our study focuses on enhancing web information retrieval and presentation by leveraging web data mining techniques to uncover intrinsic relationships within textual, linkage, and usability data. Specifically, we aim to improve the performance of web information retrieval and presentation by analysing web data features. Our approach centres on web usage mining to identify usage patterns and integrate this knowledge with user profiles for personalized content delivery. Personalization, tailored to user’s characteristics and behaviours, serves to enhance engagement, conversion, and long-term commitment to websites. The objective of our research is to develop a web personalization system that enables users to access relevant website content without the need for explicit queries. This paper presents an extensive survey of various approaches proposed by researchers in the field of web personalization. It highlights the diverse methodologies and techniques employed to enhance user experience and engagement on the web. The paper identifies key challenges that require urgent attention to advance the field of web personalization.

Exploring the Emotional Experience of Tourists in Immersive Service Scenarios: A Case Study of Super Wenheyou Restaurant

With the rapid development of the immersive service scenarios, the practical problems of the tourism industry have gradually undergone qualitative changes, and the core contradiction has changed from the simple "tourism attraction" to the more complex "tourism experience quality" problem. In the process of "immersive experience", "emotion" plays a powerful guiding role in the immersive scenarios design, guiding tourists to real-time interaction, immersive and emotional resonance. Therefore, emotional experience has become the key in the development of immersive service scenarios. This study collects the data of tourists in Wenheyou through a questionnaire of tourists' emotional experience test, and discusses the tourists' emotional experience needs, effects and improvement strategies under the background of the three-stage characteristics of immersive scenarios and emotional interaction. Tourists' emotional experience and Word frequency by ROST CM (ROST Content Mining System) 6.0, was employed for data analysis. The study elucidates that within the Wenheyou immersive service scenarios, the physical service environment, cultural stimuli, and service products serve as primary catalysts for fostering positive emotional experiences among tourists. Drawing on psychological theories and utilizing the PANAS (Positive Affect and Negative Affect Schedule) scale and Robert Plutchik's emotion wheel model, the research delves into tourists' emotional experiences within the Wenheyou immersive service scenarios. It identifies key factors shaping positive and negative emotional experiences, offering valuable insights for service enterprises to enhance tourists' emotional quality.

Collaborative Solutions to Software Architecture Challenges Faced by IT Professionals

Software architecture serves as a crucial link between problem and solution domains in software systems. However, reliance on graphical artifacts for architecture design has limitations, especially in abstract analysis. To overcome these constraints, Architecture Description Languages (ADLs) offer a more formal approach. Yet, our research reveals that ADLs face numerous challenges, as identified through interviews, surveys, and community interactions. By mining content from various sources, including mailing lists and forums, we comprehensively explore the concerns of software engineers. Employing content mining, topic modeling, and grounded theory, we compile a database of real-world issues and solutions in software architecture. Comparing our findings with existing literature, we identify 17 primary issues faced by practitioners. We also compare our results with language models to ascertain areas of agreement and disagreement. Finally, we propose solutions for each identified issue to aid future analysts.

The Experience of Stress-Related Growth Associated With COVID-19 Among Older Korean Immigrants

While some studies have investigated stress-related growth (SRG) during the COVID-19 pandemic, little research has been conducted to explore the positive psychological changes associated with COVID-19 among older Asian immigrants. Thus, we aimed to identify perceived positive changes resulting from the COVID-19 pandemic based on the SRG framework. Semi-structured in-depth interviews were conducted with 11 participants using a purposeful criterion sampling strategy. Both content mapping and content mining questions were used. Data were analyzed qualitatively using the constant comparative method. Challenges and stressors associated with the pandemic such as fears of physical or verbal attack, COVID-19 infection, strict COVID-19 regulations and mandates, and limited opportunities to interact with others were identified based on the statements of participants. We identified three salient themes that were characterized as SRG components: (a) increasing leisure-time physical activity participation, (b) developing a closer relationship with others, and (c) improving resilience. The findings of this study present qualitative evidence that older Korean immigrants to the United States experienced positive changes associated with the pandemic such as being more physically active, developing closer relationships, and improving resilience. These changes are associated with the main elements of SRG, and it appears that the pandemic may have resulted in older Korean immigrants experiencing SRG.

Decoding violence against women: analysing harassment in middle eastern literature with machine learning and sentiment analysis

The rising prevalence of harassment in Middle Eastern countries is mirrored in literary works from the region. However, extracting data from these texts to understand the typology and frequency of the cases poses a significant challenge due to human cognitive limitations and potential biases. Thus, this study aims to use natural language processing (NLP) approaches to propose a machine learning framework for text mining of sexual harassment content in literary texts. The data source for this study consists of twelve Middle Eastern novels. The proposed framework involves the classification of physical and non-physical types of sexual harassment using a machine-learning model. Lexicon-based sentiment and emotion detection are applied to sentences containing instances of sexual harassment for data labelling and analysis. Finally, a long short-term memory-gated recurrent unit (LSTM-GRU) deep learning model is built to classify the sentiment characteristics that induce sexual harassment. The proposed model achieved an accuracy of 75.8% while outperforming five other algorithms. Additionally, a sentiment classification with three labels—negative, positive, and neutral—was developed using an LSTM-GRU RNN deep learning model. The accuracy of this model was 84.5%. Most statements, even those involving physical sexual harassment, which had greater levels of sexual harassment, had negative sentiments, according to lexicon-based sentiment analysis. This study contributes to the field of text mining by providing a novel approach to identifying instances of sexual harassment in literature in English from the Middle East. The use of machine learning models and sentiment analysis techniques allows for more accurate identification and classification of different types of sexual harassment. Furthermore, this study sheds light on the prevalence of sexual harassment in Middle Eastern countries and highlights the need for further research and action to address this issue.

Does the Tourist Appreciate Your Employees? Prioritizing Service Employee Improvements Based on User-Generated Content Mining

While a multitude of opinion mining studies have examined the impact of attribute performance on customer satisfaction, they have offered limited insights into the role performance of service employees by investigating tourists’ experiences. Focusing on the hotel industry, this study investigated tourists’ evaluations of service personnel by scrutinizing 68,439 hotel reviews and explored the asymmetric effects of employee performance on customer satisfaction according to service employee functions and traveler types. The results indicate that different travelers have distinct expectations of different kinds of services, thereby moderating the asymmetric impact of role performance on tourist satisfaction. Manager employee, in particular, constitutes an excitement factor for family, solo, and couple travelers. The findings have implications for the formulation of operation strategies to promote tourists’ satisfaction rather than frustration. This study contributes to the literature by offering a novel understanding of the asymmetric effect of customer–personnel interactions on tourist satisfaction.

How social organizations participate in social governance in China: Official media's attention distribution analysis (1949-2021).

The attitude of the Chinese government towards social organizations (SOs) is crucial, as it affects the management rule and development tendency of SOs. To research the rule of SOs' participation in social governance in China, this study used a new historical perspective, the institutional development perspective, to conduct its exploration. This perspective provides an accurate measure of the reality of the SOs' participation, as it involves a mixed research methodology using continuous data from 73 years of reports and content mining, as well as topic clustering analysis to reveal a macroscopic and multi-line picture. Using a co-word analysis of hundreds of reports, from 1949-2021, in the People's Daily, an official newspaper of the Communist Party of China, this study quantified changes in intensity, emotion, and content regarding social organization participation in social governance through topic distribution. Three trends were revealed: (1) "social-oriented character" and "organized-oriented character" were identified during the change in SOs; (2) the extent of being managed gradually strengthened and shifted from the Communist Youth League of China to the Community Party of China; (3) the goals of SOs shifted from general to innovated function in special charitable organizations. The institutional development perspective can complement the focus event perspective, including a new method, co-word analysis, to examine official Chinese media and validate the Administrative Absorption of Society (AAS) theory by identifying two lines of topic clustering trends. The attention distribution analysis in official media from an institutional development perspective can help explore the role of official media reports in analyzing the allocation of national attention and provide new analytical methods for big data mining to establish the social and organizational natures of SOs to optimize their roles. It offers a basis for modern social governance policy innovation in China.

Graph Based Cross-Channel Transform for Color Image Compression

Adaptive transform coding is gaining more and more attention for better mining of image content over fixed transforms such as discrete cosine transform (DCT). As a special case, graph transform learning establishes a novel paradigm for the graph-based transforms. However, there still exists a challenge for graph transform learning-based image codecs design on natural image compression, and graph representation cannot describe regular image samples well over graph-structured data. Therefore, in this article, we propose a cross-channel graph-based transform (CCGBT) for natural color image compression. We observe that neighboring pixels having similar intensities should have similar values in the chroma channels, which means that the prominent structure of the luminance channel is related to the contours of the chrominance channels. A collaborative design of the learned graphs and their corresponding distinctive transforms lies in the assumption that a sufficiently small block can be considered smooth, meanwhile, guaranteeing the compression of the luma and chroma signals at the cost of a small overhead for coding the description of the designed luma graph. In addition, a color image compression framework based on the CCGBT is designed for comparing DCT on the classic JPEG codec. The proposed method benefits from its flexible transform block design on arbitrary sizes to exploit image content better than the fixed transform. The experimental results show that the unified graph-based transform outperforms conventional DCT, while close to discrete wavelet transform on JPEG2000 at high bit-rates.

Strengthening Supply Chain Visibility With Blockchain: A PRISMA-Based Review

Over the last 5 years, research into the use of blockchain technology in supply chains (SCs) has increased significantly. This is because many firms are faced with the necessity of enhancing SC visibility not just to fulfill statutory obligations, but also to streamline operations, assure the quality of deliverables, and ensure the sustainability of activities. This review article provides a summary of all scientific work published since 2016, with a special emphasis on the business management & accounting and decision science domains, in order to better comprehend the constantly rising knowledge clusters linked with this topic. The prominent contributors and knowledge clusters were identified using suitable bibliometric algorithms on a review corpus of 308 articles extracted from the SCOPUS database. There are five identified knowledge clusters: 1) blockchain for food SC transparency, 2) distributed ledger for sustainable SCs, 3) traceability systems using smart contracts, 4) internet of things for logistics, and 5) the emergence of Ethereum and hyperledger in SCs. This scholarly assessment, in conjunction with PageRank analysis and content mining, assisted in identifying research articles that are prominent in each cluster, which in turn helped in formulating future research avenues for aspiring researchers.

Research on the Improvement Path of New Rural Cooperative Financial Legal System Based on Decision Tree Modeling

Abstract The improvement of a new rural financial legal system can protect the legitimate rights and interests of rural farmers. In this paper, after preprocessing the text of the legal system, the Word2vec word vector model is used to represent the legal text features, select the legal text features using information gain, extract the legal text features based on the LDA topic model, and complete the construction of the legal text feature engineering. On this basis, the random forest algorithm based on the decision tree is improved by text similarity, and the text classifier is constructed to realize the mining of legal text content. After verifying the performance of legal system text mining, the relevant high-frequency words of the legal system are extracted to explore the path of improving the legal system of new rural cooperative finance. The results show that among the 18 high-frequency words extracted, finance (298 times), report (242 times), management (221 times), and statistics (221 times), all of them are more than 200 times, focusing on the financial development as the top priority for the improvement of the legal system of new rural cooperative finance. Based on the research of this paper, it is important and positive significance to improve the legal system of new rural cooperative finance, which is the key to realizing rural revitalization.

A Review Of TextMining Techniques: Trends, and Applications In Various Domains

Text mining, a subfield of natural language processing (NLP), has received considerable attention in recent yearsdue to its ability to extract valuable insights from large volumes of unstructured textual data. This review aims toprovide a comprehensive evaluation of the applicability of text mining techniques across various domains andindustries.The review starts off with a dialogue of the basic ideas and methodologies that are concerned with textual contentmining together with preprocessing, feature extraction, and machine learning algorithms.Furthermore, this survey highlights the challenges faced at some stage in implementing textual content miningstrategies. Additionally, the review explores emerging tendencies and possibilities in text-mining research. Itdiscusses advancements in deep learning models for text evaluation, integration with different AI technologies likeimage or speech recognition for multimodal analysis, utilization of domain-unique ontologies or information graphsfor more desirable information of textual facts, and incorporation of explainable AI strategies to improveinterpretability. The findings from this overview are analyzed to identify common developments and patterns in textmining packages across extraordinary domain names.The consequences of this paper will advantage researchers by means of imparting updated expertise of modernpractices in textual content mining. Additionally, it will manual practitioners in selecting suitable strategies for theirunique application domain names while addressing capacity-demanding situations.

Web Mining overview: Techniques, Tools, and Ethical Implications

In an era of unprecedented data growth, web mining has become essential for uncovering actionable insights from the vast expanse of online information. This study provides a comprehensive overview of web mining, focusing on extracting valuable insights such as user behavior patterns, trend identification, and novel information discovery. It categorizes web mining into three pivotal areas: web content mining, web structure mining, and web usage mining, and highlights key techniques, tools, and algorithms associated with each. The paper addresses critical challenges including data privacy, algorithmic bias, and the management of unstructured data, while also examining ethical considerations like user consent and data security. The unique contributions of this study lie in its detailed comparison of existing methodologies and the introduction of a new framework designed to tackle ethical concerns. Aimed at both researchers and practitioners, this paper offers actionable insights to advance the field of web mining, underscoring its interdisciplinary applications and the imperative of ethical vigilance.

CCTV-1 MEDIA TRENDS: DIVERSITY, INTERACTIVITY, AND PERSONALIZATION

Nowadays, as digital technologies gradually mature, CCTV-1 programs are showing new trends of diversification, interactivity and personalization. For example: cross-platform broadcasting, diversifica- tion of program content, increased interactivity between programs and audiences, personalized pro- grams, etc. This study aims to explore the new trends of CCTV-1 programs in the context of digitalization and analyze its impact on traditional media. Programs will cover a wider range of topics and formats to meet the needs and interests of different audience groups. These new trends not only improve the quality and standard of CCTV-1 programs, but also increase the attractiveness and influence of the pro- grams. The research methodology is based on the analytical approach to advantages of digitalization and comparative analysis of innovative and traditional ways of the content representation as well as target audience involvement. Under the background of digitalization, the new trend of CCTV-1 programs also faces challenges such as market competition, content innovation and technology application. As a result of our research, it is shown that CCTV-1 programs need to strengthen technological innovation, deepen content mining, expand interactive channels and strengthen brand building in the digital transformation to adapt to the needs and expectations of audiences in the digital era. It is of paramount importance to implement cross-platforming strategies and interactive approach in news programs.

Emerging Trends of web Mining Through Cloud Mining (Bitcoin) in Business Companies

In this paper we show research about how to mine valuable knowledge on the web mining through cloud mining in business companies and comparison about web mine. This paper illustrates the recent, previous, and upcoming web mining by cloud mining. Now we initiate real-time data set for recovery facts on the network i.e., web content mining, and the detection of client approach relationships from cloud servers, i.e., web management mining that enhance the web mining problems. Moreover, we similarly illustrated web mining through cloud mining in business companies. Cloud mining is an upcoming Web Mining. That is the main benefit of the company looking after all the usual mining problems. Cloud mining decreases the costs correlated with running a mining rig. Cloud mining is a procedure to mine cryptocurrency like bitcoin, by leased cloud computing operate without connecting or promptly governing the hardware and associated software. The initial processor that has observed a result to the problem catches the succeeding Bitcoin block, and the procedure remains. Bitcoin mining needs advanced hardware to explain difficult calculations and arithmetic challenges. In this paper we have discussed to work and is beneficial for business companies. We have proposed a structure for a cloud mining service. These services are supported by business model and strategy, hardware procurement and setup, user interface and dashboard and customer support and education etc. Cloud mining service deals are often tricks, or rip-offs. Cloud mining suppliers and companies benefit by leasing away their hardware in replace for funds. Trading mining hardware seems like a prospect’s agreement for saving ruses.

Utilizing RoBERTa Model for Churn Prediction through Clustered Contextual Conversation Opinion Mining

In computational study and automatic recognition of opinions in free texts, certain words in sentences are used to decide its sentiments. While analysing each customer’s opinion per time in churn management will be effective for personalised recommendations. Oftentimes, the opinion is not sufficient for contextualised content mining. While personalised recommendations are time consuming, it also does not provide complete picture of an overall sentiment in the business community of customers. To help businesses identify widespread issues affecting a large segment of their customers towards engendering patterns and trends of different customer churn behaviour, here, we developed a clustered contextualised conversation as opinions set for integration with Roberta Model. The developed churn behavioural opinion clusters disambiguated short messages while charactering contents collectively based on context beyond keyword-based sentiment matching for effective mining. Based on the predicted opinion threshold, customer churn category for group-based personalised decision support was generated, with matching concepts. The baseline RoBERTa model on the contextually clustered opinions, trained with a batch size of 16, a learning rate of 2e-5, over 8 epochs, using a maximum sequence length of 128 and standard hyperparameters, achieved an accuracy of 92%, Precision of 88%, Recall of 86% and F1 score of 84% over a test set of 30%.

A Comparative Study of Perceptions of Destination Image Based on Content Mining: Fengjing Ancient Town and Zhaojialou Ancient Town as Examples

Ancient canal towns in Jiangnan have become important tourist destinations due to their unique water town scenery and historical value. Creating a unique tourist image boosts these ancient towns’ competitive edge in tourism and contributes significantly to their preservation and growth. The vast amount of data from social media has become an essential source for uncovering tourism perceptions. This study takes two ancient towns in Shanghai, Zhaojialou and Fengjing, as case study areas. In order to explore and compare the destination images of the towns, in the perception of tourists and in official publicity, machine learning approaches like word embedding and K-means clustering are adopted to process the comments on Sina Weibo and publicity articles, and statistical analysis and correspondence analysis are used for comparative study. The results reveal the following: (1) Using k-means clustering, destination perceptions were categorized into 16 groups spanning three dimensions, “space, activity, and sentiment”, with the most keywords in “activity” and the fewest in “sentiment”. (2) The perception of tourists often differs significantly from the official promotional materials. Official promotions place a strong emphasis on shaping the image of ancient towns based on their historical resources, presenting a more general picture. Tourist perception, which is fragmented, highlights emerging elements and the experiential activities, along with the corresponding emotional experiences. (3) Comparing the two towns, Fengjing Ancient Town stands out, with more diverse tourist perceptions and richer emotional experiences. This underscores the effectiveness of tourism activities that use space as a media to evoke emotions, surpassing the impact of the spaces themselves.