Keyword Extraction Research Articles

Expert Profile Identification From Community Detection on Author-Publication-Keyword Graph With Keyword Extraction

Expert Profile Identification From Community Detection on Author-Publication-Keyword Graph With Keyword Extraction

Research on the TF–IDF algorithm combined with semantics for automatic extraction of keywords from network news texts

Research on the TF–IDF algorithm combined with semantics for automatic extraction of keywords from network news texts

Granting and Limiting Divorce in Ancient China in the Context of Big Data Technology

Abstract Divorce has been a significant societal issue since antiquity, warranting careful scholarly attention. The evolution of divorce systems from ancient times to the present day mirrors broader societal, ideological, and marital transformations. This paper presents an analytical exploration of the social implications of ancient divorce legislation through the lens of four historical divorce models. By assembling a corpus of ancient divorce texts and employing the vector space model for data representation, this study leverages the TF-IDF algorithm for keyword extraction. Subsequently, it utilizes a keyword co-occurrence network to scrutinize pivotal elements within the ancient divorce systems. Furthermore, the study employs the Latent Dirichlet Allocation (LDA) model to investigate the thematic evolution of these systems. Analysis of the corpus reveals that the term “separation” appears 513 times, underscoring its prevalence. The keyword co-occurrence network exhibits a point degree centrality of 0.523, indicating significant interconnectivity. The principal legal term, “shall,” occurs 68 times, reflecting its normative importance. Notably, the study identifies subtle variations in legal stipulations across different dynasties, primarily concerning changes in the grounds and moral considerations for divorce. These historical divorce systems were profoundly influenced by the dynamics of marital relationships, as well as patriarchal, familial, and societal constraints. This historical perspective offers valuable insights for contemporary marital law implementations, suggesting a nuanced understanding of divorce’s legal and social dimensions.

Exploring Human-Centricity in Industry 5.0: Empirical Insights from a Social Media Discourse

The transition from Industry 4.0 towards Industry 5.0 marks a paradigm shift, emphasising human-centricity in industrial settings. Industry 5.0 focuses on improving the future role of people in addition to merely technological progress. While "human centricity" gains recognition, ambiguity surrounds its definition and application. The literature lacks clear consensus on the concept and its industrial implications. This paper provides clarity on human-centricity by analysing viewpoints and public opinions based on posts published on LinkedIn in the last five years regarding human-centricity. The analysis involved text mining techniques, including semantic clustering to discover distinct clusters of discussions related to human-centricity and keywords extraction to tag the different clusters. The findings reveal that public opinion predominantly centres on the skills required by future workers, encompassing both hard and soft skills, as well as social themes such as gender equity and workplace comfort. This research underscores the critical relevance of these components in the transition towards Industry 5.0, offering valuable insights for industrial practitioners and researchers alike.

Student Engagement in a Gamified Online Learning Environment: A Data Mining Approach

In education, gamification applies game design elements to non-game contexts to engage learners. This preliminary study explored the feasibility of using data mining to analyze student engagement patterns in an asynchronous online course that utilized gamification. We conducted a series of exploratory data analysis approaches, including keyword extraction, clustering, concordance analysis, and sequential pattern mining. Results showed peaks in engagement aligned with gamified challenges and rewards. Students discussed both academic and non-academic topics, indicating community-building. Sequence pattern mining revealed more consistent academic engagement for highly engaged students. Findings demonstrate the viability of using data mining techniques to assess cognitive engagement based on discussion patterns in a gamified online course.

An Accuracy Study of Personalized Recommendation System for E-commerce Based on Big Data Analysis

Abstract E-commerce, as an emerging value chain model for the global economy, has greatly promoted development, while the impact of digitalization on traditional publishing enterprises is increasingly evident. In this paper, we propose a TextRank keyword extraction algorithm based on comprehensive weights, which extracts and assigns keywords that identify user information, behavior, and product characteristics. We then output a keyword weight table for user information, user behavior, and product keywords. Finally, utilizing an optimized collaborative filtering recommendation algorithm, we establish a recommendation model between the user-commodity matrix to build an e-commerce personalized recommendation system that provides users with more accurate customized recommendations. The practical application of the designed personalized recommendation system is examined to evaluate its accuracy. The MAE of this algorithm is smaller than that of user-based (0.8915, 0.9470) or item-based (0.8873, 0.9327) collaborative filtering algorithms, indicating that the improved collaborative filtering algorithm effectively enhances system recommendation accuracy. The direct effect value of recommendation strength is 0.344, with an indirect effect value of 0.018, leading to the highest overall effect value. This study provides users with convenient and attentive services, significantly enhances user experience quality, and generates substantial profits for the e-commerce platform.

The role of social media data analytics in rural tourism market trend forecasting

Abstract The rapid development of social media provides new means for market trend and quotation prediction. This paper realizes market trend prediction by performing TF-IDF keyword extraction and vectorization on social media data, improving the deep typical correlation analysis to realize semantic mining, and constructing a consumer intention mining method based on social media data. The rural tourism market trend prediction for Haikou City, Hainan Province, China focuses on examining the number of tourists and the economic income of rural tourism. The future number of inbound tourists and the total number of tourists in Haikou City will still show an upward trend overall, with the total number of tourists predicted to reach 73,641,100 in 2028. With the continuous development of rural tourism, the tourism economy of Haikou City reaches 67.068 billion in 2022, an increase of 259.67% from 2008, and is predicted to double in 2027, and is expected to reach 148.282 billion in 2028. The number of overseas tourists and foreign exchange earnings from rural tourism will continue to increase.

Tapping the Economic Potential and Optimizing Strategies of Intangible Cultural Heritage under Digital Transformation

Abstract Intangible cultural heritage has important historical, cultural, and spiritual values. This paper selects the intangible cultural heritage of Yunnan Painting, the national architectural decoration of Dali, as the case study object. We use web crawler technology to extract the consumer’s comment text data, establishing the Yunnan Painting consumer comment text dataset. Secondly, we extract high-frequency words from the dataset using text mining technology, TF-IDF keyword extraction, and LDA themes, and cluster them using the K-means method. Finally, we evaluate the economic potential of Yunnan Painting in terms of consumers’ cognitive abilities and the emotional appeal of its products, based on the extracted results. The study reveals that the dressmaking, painting, and tourism industries are the main economic activities of Yunnan color painting, with the tourism industry requiring further strengthening. According to the text mining of high-frequency words, the frequency of “product” in Yunnan painting was the highest at 296 times, followed by “culture,” “technology,” “cultural creativity,” and “theme.” The analysis of consumers’ emotional text features reveals that most consumers have positive emotions towards Yunnan-colored paintings, while very few have negative emotions. The economic optimization strategy that is proposed is based on the results and provides a reference value for the economic development of other intangible cultural heritage.

Keyword extraction method based on complex network

Keyword extraction method based on complex network

Research on Sentiment Analysis Techniques for Online Ideological and Political Education

Abstract Network ideological and political education is a great extension of school education, but the complex and confusing network information is also easy to give students bad emotional guidance. In this paper, microblogging data and topic clustering technology are used to obtain the emotional text data of network ideological and political education, based on the LDA model for topic feature extraction, to remove the data that has a lower degree of relevance to ideological and political education emotion in the data. Then, combined with the Jieba segmentation tool, based on the TF-IDF algorithm, for keyword extraction of the data that has been segmented. The text semantic features extracted based on the Word2Vec word vector model are used as the input information of this model, and the unique memory unit and gating structure of LSTM are utilized to solve the problem of storing memory of a priori knowledge and the problem of gradient dissipation caused by too long time threshold in long text classification. The Word2Vec-LSTM text sentiment classification model is developed and trained using Python and related machine learning libraries, and comparative experiments are carried out to validate the algorithm’s effectiveness and feasibility. The performance of Word2Vec-LSTM sentiment analysis on the dataset is higher than Word2Vec and LSTM algorithms. In addition, the value of sentiment perplexity is the smallest when the number of topics is set to be about 8, and its value is 0.542, and the positive sentiment density reaches the maximum value (0.648) as the sentiment score grows to 0.48. This paper explores the application of emotion analysis results in network ideological and political education to further improve the construction of a network ideological and political education system.

Knowledge Mapping Applied to the Construction of a Theoretical Resource Base for Teaching English at the University Level

Abstract In the evolving field of educational technology, knowledge mapping has emerged as a pivotal tool for university English education. This study examines its role in building a theoretical resource base to enhance the efficiency of organizing, accessing, and utilizing teaching materials. This research constructs a knowledge graph for English teaching resources by employing a seven-step process to create a domain ontology and an enhanced TF-IDF algorithm for keyword extraction. Results show the knowledge graph significantly boosts information retrieval accuracy and user satisfaction. The experimental group reported a 25% increase in learning efficiency, a 30% enhancement in the quality of learning outcomes, and a 12.63% rise in average grades, outperforming the control group. The findings suggest knowledge mapping substantially benefits the development of a theoretical resource base for university English teaching, improving teaching and learning quality.

Combining sentiment analysis and text mining with content analysis of farm vet interviews on mental wellbeing in livestock practice.

The aim of the How Farm Vets Cope project was to co-design, with farm veterinary surgeons, a set of web-based resources to help them and others deal with the different situations that they can face. As part of the wider project, participants were recruited for one-to-one semi-structured phone interviews. These interviews focused on elements of job satisfaction and how the participants coped during periods of poor mental wellbeing or with setbacks and failure. Transcripts of these interviews were analysed using both quantitative methods of sentiment analysis and text mining, including term frequency/inverse document frequency and rapid automated keyword extraction, and qualitative content analysis. The twin aims of the analysis were identifying the important themes discussed by the participants and comparing the results of the two methods to see what differences, if any, arose. Analysis using the afinn and nrc sentiment lexicons identified emotional themes of anticipation and trust. Rapid automated keyword extraction highlighted issues around age of vets and support, whilst using term frequency/inverse document frequency allowed for individual themes, such as religion, not present across all responses, to be identified. Content analysis supported these findings, pinpointing examples of trust around relationships with farmers and more experienced vets, along with some examples of the difference good support networks can make, particularly to younger vets. This work has confirmed previous results in identifying the themes of trust, communication and support to be integral to the experience of practicing farm veterinary surgeons. Younger or less experienced vets recognised themselves as benefiting from further support and signposting, leading to a discussion around the preparation of veterinary students for entry into a farm animal vet practice. The two different approaches taken showed very good agreement in their results. The quantitative approaches can be scaled to allow a larger number of interviews to be utilised in studies whilst still allowing the important qualitative results to be identified.

Sustainable Urban Planning and Genius Loci: A LDA-Based Analysis of Recreational Landscape Imagery in Hangzhou’s Qiaoxi Historic District

Amidst the impacts of urbanization and commercialization, the cultural and historical essence of neighborhoods is undermined, paralleled by the diminishing Genius Loci. This research explores the previously under-researched area of how urban leisure districts’ spatial design influences tourists’ experiential imagery perceptions and the Genius Loci, aiming to elucidate their dialectical relationship. Leveraging LDA topic modeling and TF-IDF text analysis, we distill behavioral and cognitive imageries from tourist reviews to forge a perception framework of the Genius Loci, bifurcated into spiritual and directional imagery dimensions. Secondary dimensions emerge through TF-IDF keyword extraction. GIS technology further analyzes the spatial distribution of emotional imageries and the spatiotemporal dynamics of emotions, highlighting its significance in sustainable urban development. The findings reveal the multidimensional experiential imageries of tourists in leisure neighborhoods and their impact and feedback on the Genius Loci, offering a new perspective on the interaction between tourists’ emotional spatial experiences and the Genius Loci. It is discovered that tourist perceptions mainly concentrate on “environmental psychological perception dimension” and “spatial orientation perception dimension” with the spatial distribution of tourists’ emotional experiences showing significant temporal dynamics. These results underline a complex interaction between tourists’ experiential imagery and the Genius Loci, suggesting that tourists’ feedback could play a critical role in shaping the evolution of these cultural spaces, emphasizing the need for integrated approaches in urban planning and heritage conservation.

A Natural Language Processing Model on BERT and YAKE Technique for Keyword Extraction on Sustainability Reports

A Natural Language Processing Model on BERT and YAKE Technique for Keyword Extraction on Sustainability Reports

Keyword extraction method based on complex network

Keyword extraction method based on complex network

Integration Strategies of Sports Culture in Ethnic Areas and Volleyball Teaching in Colleges and Universities

Abstract The integration of ethnic area sports culture and college sports teaching in the multicultural environment is the inevitable development of education and teaching in the new era. This paper combines the analysis of the impact of the school sports curriculum in a multicultural environment and the form of development of sports culture, combines the sports culture of ethnic areas with college volleyball teaching in order to promote the implementation of sports ideological and political education in colleges and universities and to realize the development of differences and unity in the context of multivariate evaluation of college volleyball teaching. Combine the objectives of college volleyball teaching with the national sports culture to construct the volleyball course content system. Propose methods for training teachers, create a knowledge map of college volleyball teaching materials, and use the enhanced TF-IDF algorithm to extract keywords and increase volleyball teaching resource allocation. Statistics of volleyball teaching in colleges and universities integrating national sports culture, analyzing the influence of learning atmosphere*teaching level, learning atmosphere*attention on the cultivation of national sports culture. Combine the improvement of volleyball skills, physical quality, and independent learning ability before and after teaching to analyze the advantages of college volleyball teaching that integrates ethnic sports culture. The overall mean value of “the current situation of volleyball teaching integrating ethnic sports culture” is 8.97, which is at a good level. In the three dimensions of learning motivation, learning process, and learning outcome, the P-value of the experimental class and the control class is less than or equal to 0.05, which indicates that the students in the experimental class are better than the students in the control class in the three dimensions of independent learning ability in sports. Volleyball teaching that integrates the sports culture of ethnic regions has the beneficial effect of promoting the development of sports culture as well as enhancing the development of students’ volleyball skills.

Quantitative evaluation of science and technology financial policies based on the PMC-AE index model: A case study of China's science and technology financial policies since the 13th five-year plan.

The formulation of science and technology financial policies directly influences the direction of national economic development. Quantitative evaluation of these policies is an important method to reflect the consistency and strengths and weaknesses of policy interrelations. This paper analyzes 16 science and technology financial policy documents issued by the Chinese central government from 2016 to 2022, using text analysis and content analysis to extract keyword frequencies, and constructs 9 primary variables and 34 secondary variables. For the first time, a PMC-AE index model for science and technology financial policies is established, and a quantitative evaluation is conducted on 5 significant policy documents out of the 16. The results show that, from an overall analysis, Policy 1 and Policy 4 are at a good level, while the other three policies are at an excellent level. From the analysis of individual policy PMC-AE indexes, the rankings in descending order are: P2 > P5 > P3 > P4 > P1. Overall, the policies effectively meet the needs of China's science and technology financial development, with P2, P3, and P5 being at an excellent level, P4 at a good level, and P1 at an acceptable level, mainly reflecting the need for improvement in aspects such as policy synchronization with the current stage, targeted entities, guiding fields, and policy content. It is recommended that Chinese government departments should focus on five aspects in policy formulation: building a talent system for science and technology finance, improving the quality of financial services, coordinating central and local financial policies, protecting intellectual property rights in science and technology finance, and strengthening financial supervision. This will be conducive to the effective implementation of science and technology financial policies.

역대 대통령의 광복절 연설문 어휘 양상 연구

This study aimed to extract feature extraction keywords using BigKinds for 68 speeches of the President of the Korea on Independence Day and present the current status of related words in sentences through the extracted keywords. As a research method, we used BigKinds, which applies an automatic algorithm of text mining method, to extract feature extraction keywords centered on nouns or noun phrases. Among national holidays, speeches on Independence Day emphasized Korea's history and identity, peaceful unification and security, democratization and democracy, economy and culture for the future of Korea, and people's efforts and cooperation. The purpose of Korea's democracy and freedom, efforts for our country's independence and unification, and expressions of joy at liberation and gratitude for the sacrifices of the ancestors were frequently used. In addition, for peaceful unification, efforts were made to ease inter-Korean tensions and promote inter-Korean economic cooperation, and words to achieve economic growth were used to strengthen security. Presidential speeches show lexical characteristics that reveal core values depending on domestic and international situations for each president, and it is necessary to continue presidential speech research and expand the data.

언어 네트워크 분석기법을 통한 생태(환경)교육 기본계획 문서 분석

Objectives The main purpose of this study is to determine what the key words are in the basic plans for ecological(environmental) education created by provincial offices of education and what the relationships are between those words. In addition, this study was conducted to determine whether the documents of the provincial office of education contain the value of ecological transformation in education.
 Methods The research method used in this study is semantic network analysis. The words included in the basic plans for ecological(environmental) education created by the provincial offices of education in 13 regions were corrected, controlled, and removed to extract key words, and the frequencies of the words were analyzed. Additionally, basic attribute analysis, centrality analysis, sub-network analysis, and ego network analysis were performed using Gephi 0.10.1, a network analysis program.
 Results The results of this study are as follows. First, as a result of analyzing the frequency of use and centrality of each key words, ‘practice’ and ‘carbon neutrality’ appeared most frequently and have the highest influence in the documents. Second, as a result of clustering key words according to their similarity, these words formed four sub-networks. Third, as a result of examining the connection structure between ‘ecological transformation education’ and ‘ecological environment education’ through ego network analysis, the two words shared a lot of related words.
 Conclusions The conclusions of this study are as follows. First, The use of ‘practice’ and ‘carbon neutrality’ as key words in the basic plans for ecological (environment) education should be maintained in the future. At this time, educational activities at the cognitive and affective levels should also be treated with great importance in the document. Second, since ecological transformation education and ecological environment education form different clusters, each documents must establish a mutually cooperative and complementary relationship. Third, many words are shared between the ecological transformation ego network and the ecological environment education ego network, and since there is ongoing discussion about what term to replace the existing environmental education with, so provincial offices of education should be cautious in using the terms.

ChatReview: A ChatGPT-enabled natural language processing framework to study domain-specific user reviews

Intelligent search engines including pre-trained generative transformers (GPT) have revolutionized the user search experience. Several fields including e-commerce, education, and hospitality are increasingly exploring GPT tools to study user reviews and gain critical insights to improve their service quality. However, massive user-review data and imprecise prompt engineering lead to biased, irrelevant, and impersonal search results. In addition, exposing user data to these search engines may pose privacy issues. Motivated by these factors, we present ChatReview, a ChatGPT-enabled natural language processing (NLP) framework that effectively studies domain-specific user reviews to offer relevant and personalized search results at multiple levels of granularity. The framework accomplishes this task using four phases including data collection, tokenization, query construction, and response generation. The data collection phase involves gathering domain-specific user reviews from public and private repositories. In the tokenization phase, ChatReview applies sentiment analysis to extract keywords and categorize them into various sentiment classes. This process creates a token repository that best describes the user sentiments for a given user-review data. In the query construction phase, the framework uses the token repository and domain knowledge to construct three types of ChatGPT prompts including explicit, implicit, and creative. In the response generation phase, ChatReview pipelines these prompts into ChatGPT to generate search results at varying levels of granularity. We analyze our framework using three real-world domains including education, local restaurants, and hospitality. We assert that our framework simplifies prompt engineering for general users to produce effective results while minimizing the exposure of sensitive user data to search engines. We also present a one-of-a-kind Large Language Model (LLM) peer assessment of the ChatReview framework. Specifically, we employ Google’s Bard to objectively and qualitatively analyze the various ChatReview outputs. Our Bard-based analyses yield over 90% satisfaction, establishing ChatReview as a viable survey analysis tool.