Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/m...Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/methodology/approach:We collected publications on CRISPR between 2011 and2020 from the Web of Science,and traced all the patents citing them from lens.org.15,904 articles and 18,985 patents in total are downloaded and analyzed.The LDA model was applied to identify underlying research topics in related research.In addition,some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents.Findings:The emerging research topics on CRISPR were identified and their evolution over time displayed.Furthermore,a big picture of knowledge transition from research topics to technological classes of patents was presented.We found that for all topics on CRISPR,the average first transition year,the ratio of articles cited by patents,the NPR transition rate are respectively 1.08,15.57%,and 1.19,extremely shorter and more intensive than those of general fields.Moreover,the transition patterns are different among research topics.Research limitations:Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org.A limitation inherent with LDA analysis is in the manual interpretation and labeling of"topics".Practical implications:Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR.Originality/value:The LDA model here is applied to topic identification in the area of transformative researches for the first time,as exemplified on CRISPR.Additionally,the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T.展开更多
Purpose:This article aims to describe the global research profile and the development trends of single cell research from the perspective of bibliometric analysis and semantic mining.Design/methodology/approach:The li...Purpose:This article aims to describe the global research profile and the development trends of single cell research from the perspective of bibliometric analysis and semantic mining.Design/methodology/approach:The literatures on single cell research were extracted from Clarivate Analytic’s Web of Science Core Collection between 2009 and 2019.Firstly,bibliometric analyses were performed with Thomson Data Analyzer(TDA).Secondly,topic identification and evolution trends of single cell research was conducted through the LDA topic model.Thirdly,taking the post-discretized method which is used for topic evolution analysis for reference,the topics were also be dispersed to countries to detect the spatial distribution.Findings:The publication of single cell research shows significantly increasing tendency in the last decade.The topics of single cell research field can be divided into three categories,which respectively refers to single cell research methods,mechanism of biological process,and clinical application of single cell technologies.The different trends of these categories indicate that technological innovation drives the development of applied research.The continuous and rapid growth of the topic strength in the field of cancer diagnosis and treatment indicates that this research topic has received extensive attention in recent years.The topic distributions of some countries are relatively balanced,while for the other countries,several topics show significant superiority.Research limitations:The analyzed data of this study only contain those were included in the Web of Science Core Collection.Practical implications:This study provides insights into the research progress regarding single cell field and identifies the most concerned topics which reflect potential opportunities and challenges.The national topic distribution analysis based on the post-discretized analysis method extends topic analysis from time dimension to space dimension.Originality/value:This paper combines bibliometric analysis and LDA model to analyze the evolution trends of single cell research field.The method of extending post-discretized analysis from time dimension to space dimension is distinctive and insightful.展开更多
This study investigates how universities are developing normativeframeworks to regulate the use of generative AI tools in higher education, with aparticular focus on balancing empowerment and discipline. Drawing on th...This study investigates how universities are developing normativeframeworks to regulate the use of generative AI tools in higher education, with aparticular focus on balancing empowerment and discipline. Drawing on theoreticallenses such as Foucault’s discipline theory and contemporary AI ethics, the paperanalyzes policy documents from institutions including Harvard, Oxford, and severaltop Chinese universities. Using Latent Dirichlet Allocation (LDA) topic modeling,the study reveals five dominant governance themes—ranging from academicintegrity enforcement to pedagogical empowerment. The findings highlight a globalshift from restrictive to balanced, ethics-informed AI governance, with significantdisciplinary variations. The paper concludes by proposing a “principledpermissiveness” model that combines transparency, accountability, andpedagogical innovation in future AI governance.展开更多
With Chinese science fiction films and TV series gradually entering the international stage,this study focuses on English-language reviews on Internet Movie Database(IMDb)and Letterboxd.Using Latent Dirichlet Allocati...With Chinese science fiction films and TV series gradually entering the international stage,this study focuses on English-language reviews on Internet Movie Database(IMDb)and Letterboxd.Using Latent Dirichlet Allocation(LDA)topic modeling,it analyzes the international reception of the Chinese sci-fi work The Three-Body Problem,aiming to uncover the main discussion points and differences in attention among foreign audiences for the two versions of The Three-Body Problem through text-mining techniques.The findings reveal differences between the two versions in terms of target audience,cultural presentation,adaptation evaluation,and focus in plot discussion.Moreover,this study explores new pathways for Chinese sci-fi media to go global from four dimensions:channel,content,communicator,and recipient.The study provides not only a novel perspective for understanding the international influence of Chinese sci-fi media but also empirical support for strategies in the international dissemination of Chinese cultural products.展开更多
The selection and coordinated application of government innovation policies are crucial for guiding the direction of enterprise innovation and unleashing their innovation potential.However,due to the lengthy,voluminou...The selection and coordinated application of government innovation policies are crucial for guiding the direction of enterprise innovation and unleashing their innovation potential.However,due to the lengthy,voluminous,complex,and unstructured nature of regional innovation policy texts,traditional policy classification methods often overlook the reality that these texts cover multiple policy topics,leading to lack of objectivity.In contrast,topic mining technology can handle large-scale textual data,overcoming challenges such as the abundance of policy content and difficulty in classification.Although topic models can partition numerous policy texts into topics,they cannot analyze the interplay among policy topics and the impact of policy topic coordination on enterprise innovation in detail.Therefore,we propose a big data analysis scheme for policy coordination paths based on the latent Dirichlet allocation(LDA)model and the fuzzyset qualitative comparative analysis(fsQCA)method by combining topic models with qualitative comparative analysis.The LDA model was employed to derive the topic distribution of each document and the word distribution of each topic and enable automatic classi-fication through algorithms,providing reliable and objective textual classification results.Subsequently,the fsQCA method was used to analyze the coordination paths and dynamic characteristics.Finally,experimental analysis was conducted using innovation policy text data from 31 provincial-level administrative regions in China from 2012 to 2021 as research samples.The results suggest that the proposed method effectively partitions innovation policy topics and analyzes the policy configuration,driving enterprise innovation in different regions.展开更多
The progress of IT technology such as social network and mobile payment and the change of social economic environment promote the emergence of sharing economy.As a subversive business model,the sharing economy is grow...The progress of IT technology such as social network and mobile payment and the change of social economic environment promote the emergence of sharing economy.As a subversive business model,the sharing economy is growing at an alarming rate all over the world.However,the influencing factors of consumers'continuous participation in the sharing economy are not clear.The paper aims to clarify the relationship between consumer perceived value and repeat purchase intention in the sharing economy.Taking the sharing economy platform(Airbnb)as an example,it proposes a dimension framework of consumer perceived value in peer-to-peer(P2P)accommodation rental service,including functional value,hedonic value,epistemic value and social relationship value.This paper used big data technology to crawl online reviews of P2P accommodation platform.LDA(Latent Dirichlet Allocation)topic model and sentiment analytics method were applied to construct the measurement indicators of perceived value based on online reviews.And repeat purchase intention variables were extracted from online reviews.Then structural equation model was used to examine the effect of perceived value dimensions on it.The paper identified that perceived value has a positive impact on consumers'repurchase intention in P2P accommodation.Also,social relationship value was considered as the most important influencing factor.展开更多
The development and utilization of new and renewable resources of energy has become an important layout of the development strategy in China.Photovoltaic industry is an important strategic emerging industry for the de...The development and utilization of new and renewable resources of energy has become an important layout of the development strategy in China.Photovoltaic industry is an important strategic emerging industry for the development and utilization of new energy in China.Therefore,it is important for the government to make policy to ensure the stable and orderly development of photovoltaic enterprises to accelerate the industrial structure transition in China.This paper collects the policies on photovoltaic industry,and then analyzes the industrial policy with Latent Dirichlet Allocation(LDA).LDA is generally used in document topic label extraction and recommendation system.However,this paper applies it to policy theme analysis to study the impact of policy information flow on the risk of photovoltaic enterprises.Previous studies on photovoltaic enterprise risk examined traditional financial indicators,such as asset-liability Ratio and ROE.However,the textual information in the industrial policy has rarely been studied to quantitatively analyze photovoltaic enterprise risk.In our proposed method,LDA is first used to extract the text features hiding in the text of the industrial policies,and deep neural networks then are trained on the data,which include the text features and traditional numeric features for predict photovoltaic enterprise risk.The experimental results show that the industrial policy of the current quarter has a significant effect on photovoltaic enterprise risk.Compared with this,the industrial policy of last quarter has a weak impact on the photovoltaicenterprise risk.The proposed model is a useful tool for the prediction of the photovoltaic enterprise risk.展开更多
The quality of logistics services affects the performance and competitiveness of express logistics companies.Conventional evaluation methods of service quality are based on constructing a rating index system and then ...The quality of logistics services affects the performance and competitiveness of express logistics companies.Conventional evaluation methods of service quality are based on constructing a rating index system and then comprehensively evaluating the associated questionnaires and interviews.Such methods are often subjective,time-consuming,and include a limited sample size(data).On the other hand,customers'opinions concerning logistics services can now be deciphered using online customer reviews.Therefore,this study proposes a method combining the models of latent Dirichlet allocation and long-short term memory(LDA-LSTM)to overcome the limitations of conventional evaluation methods.The main contributions include four aspects.First,the LDA-LSTM model can extract comprehensive aspects and opinions to develop evaluation indexes and scores for logistics service quality,and the reviews of a logistics firm can be examined to verify the effectiveness of this method.Second,the LDA-LSTM model can handle a situation in which several aspects and opinions express one topic or sentiment,and it outperforms the joint sentiment topic model(JST)and naive Bayes classification(NB).Third,positive and negative ratings can reflect a firm's overall service quality,with an excellent rating highlighting the best service quality,which can provide a multi-dimensional evaluation.Fourth,we also identify the indicators of logistics service quality on which customers focus,and we compare the service quality among express enterprises in China.展开更多
Revealing and comparing the evolution process of hot topics in the field of Digital Library in China and abroad.[Methods]:Taking data in the field of Digital Library from core journals in CKNI and Web of Science from ...Revealing and comparing the evolution process of hot topics in the field of Digital Library in China and abroad.[Methods]:Taking data in the field of Digital Library from core journals in CKNI and Web of Science from 1990 s to 2020,topics are extracted by LDA model and hot topics are selected based on life cycle theory.Topic evolution paths are generated to contrast evolution of hot topics between home and abroad which are grouped into dimensions of technology and application.It fails to analyze the lagging performance and reasons of research hot topics in the field of Digital Library at home and abroad.In technological dimension of Digital Library,the research content in China lags behind that at abroad.In terms of application dimension,Chinese application tends to focus on social sciences,while application at abroad tends to focus on natural sciences.The evolution of overall research focus is U-shaped,which gradually shifted from technological research to application research,and now turn back to technological dimension.Nowadays,there are also many emerging topics combined with big data technology.展开更多
Tracking and investigating tourist satisfaction and accurately identifying the key factors that affect tourist satisfaction have always been among the top priorities for academia and tourist attraction operators.With ...Tracking and investigating tourist satisfaction and accurately identifying the key factors that affect tourist satisfaction have always been among the top priorities for academia and tourist attraction operators.With the rise of online travel,analysis based on online comments has become an important method for tracking and surveying tourist satisfaction.This article examined the online comments of tourists for the Panjin Red Beach Scenic Corridor Scenic Area(hereinafter referred to as Red Beach)on Ctrip as an example.Using natural language processing to classify the tourist evaluations into topics,the main topics of concern were identified as tourism services,tourism attractions,scenic area management,and tourism experience.Through the 5-level rating of Ctrip’s online gaming customer satisfaction,an analysis was conducted on tourist satisfaction and the topics of greatest concern to the tourists were ranked.The results showed that the satisfaction levels from high to low are:tourism experience,tourism attractions,scenic area management,and tourism services.Therefore,satisfaction with related content under the service topic was the lowest so this aspect urgently needs to be improved and enhanced.展开更多
Architectural programming is of significant importance in improving urban planning efficiency,enhancing the sustainability of the construction industry,and supporting the development of social infrastructure.However,d...Architectural programming is of significant importance in improving urban planning efficiency,enhancing the sustainability of the construction industry,and supporting the development of social infrastructure.However,due to its broad scope and complex content,research in this area exhibits diverse and scattered characteristics.To comprehensively understand the current status,progress,and future directions of architectural programming research,and to provide recommendations for both China and other countries,this study conducted a comparative review of relevant literature using a mixed method approach of bibliometrics,latent Dirichlet allocation(LDA)topic modeling,and systematic review.The study summarized the differences between China and other countries in six topics within the field of architectural programming:(1)intelligent and digital methods,(2)sustainability and practices,(3)project management and decision support,(4)urban planning and existing development,(5)social infrastructure and public services,and(6)architectural education and talent cultivation.This study provides an overview and comparative analysis on architectural programming,offering important reference for future research and practice,and promoting the overall development of the construction industry.展开更多
In the modern economy,startups are not only significant drivers of innovation and technological progress but also key players in addressing employment issues and promoting economic diversification.However,startups oft...In the modern economy,startups are not only significant drivers of innovation and technological progress but also key players in addressing employment issues and promoting economic diversification.However,startups often face substantial operational risks and uncertainties in their early stages,especially regarding financing.To uncover the impact of different resource allocations and strategic choices on financing success,this study proposes a predictive method based on the latent Dirichlet allocation(LDA)topic model and deep neural networks through an in-depth analysis of startup financing cases.We systematically collected description text data from 2,000 startups and extracted text features from these descriptions using the LDA topic model.These features,combined with several traditional numerical indicators such as industry,product type,technology type,number of employees,and company size,were used to train a deep neural network to predict startup financing outcomes.The experimental results show that the prediction performance based on the LDA topic model is significantly better than that of traditional models relying solely on numerical data.This highlights the importance of text features in predicting the success of startup financing.展开更多
With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring di...With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring difficulties for potential consumers to summary all the heterogenous reviews forreference. This paper proposes a new ranking method through online reviews based on differentaspects of the alternative products, which combines both objective and subjective sentiment values.Firstly, weights of these aspects are determined with LDA topic model to calculate the objectivesentiment value of the product. During this process, the realistic meaning of each aspect is alsosummarized. Then, consumers＇ personalized preferences are taken into consideration while calculatingtotal scores of alternative products. Meanwhile, comparative superiority between every two productsalso contributes to their final scores. Therefore, a directed graph model is constructed and the finalscore of each product is computed by improved PageRank algorithm. Finally, a case study is given toillustrate the feasibility and effectiveness of the proposed method. The result demonstrates that whileconsidering only objective sentiment values of the product, the ranking result obtained by our proposedmethod has a strong correlation with the actual sales orders. On the other hand, if consumers expresssubjective preferences towards a certain aspect, the final ranking is also consistent with the actualperformance of alternative products. It provides a new research idea for online customer review miningand personalized recommendation.展开更多
基金supported by the National Natural Science Foundation of China,Grant numbers:71974167 and 71573225。
文摘Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/methodology/approach:We collected publications on CRISPR between 2011 and2020 from the Web of Science,and traced all the patents citing them from lens.org.15,904 articles and 18,985 patents in total are downloaded and analyzed.The LDA model was applied to identify underlying research topics in related research.In addition,some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents.Findings:The emerging research topics on CRISPR were identified and their evolution over time displayed.Furthermore,a big picture of knowledge transition from research topics to technological classes of patents was presented.We found that for all topics on CRISPR,the average first transition year,the ratio of articles cited by patents,the NPR transition rate are respectively 1.08,15.57%,and 1.19,extremely shorter and more intensive than those of general fields.Moreover,the transition patterns are different among research topics.Research limitations:Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org.A limitation inherent with LDA analysis is in the manual interpretation and labeling of"topics".Practical implications:Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR.Originality/value:The LDA model here is applied to topic identification in the area of transformative researches for the first time,as exemplified on CRISPR.Additionally,the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T.
基金the Chinese Academy of Sciences literature information capability construction project of 2020“Construction of strategic information research and consultation system in science and technology field”(Grant No.E290001)。
文摘Purpose:This article aims to describe the global research profile and the development trends of single cell research from the perspective of bibliometric analysis and semantic mining.Design/methodology/approach:The literatures on single cell research were extracted from Clarivate Analytic’s Web of Science Core Collection between 2009 and 2019.Firstly,bibliometric analyses were performed with Thomson Data Analyzer(TDA).Secondly,topic identification and evolution trends of single cell research was conducted through the LDA topic model.Thirdly,taking the post-discretized method which is used for topic evolution analysis for reference,the topics were also be dispersed to countries to detect the spatial distribution.Findings:The publication of single cell research shows significantly increasing tendency in the last decade.The topics of single cell research field can be divided into three categories,which respectively refers to single cell research methods,mechanism of biological process,and clinical application of single cell technologies.The different trends of these categories indicate that technological innovation drives the development of applied research.The continuous and rapid growth of the topic strength in the field of cancer diagnosis and treatment indicates that this research topic has received extensive attention in recent years.The topic distributions of some countries are relatively balanced,while for the other countries,several topics show significant superiority.Research limitations:The analyzed data of this study only contain those were included in the Web of Science Core Collection.Practical implications:This study provides insights into the research progress regarding single cell field and identifies the most concerned topics which reflect potential opportunities and challenges.The national topic distribution analysis based on the post-discretized analysis method extends topic analysis from time dimension to space dimension.Originality/value:This paper combines bibliometric analysis and LDA model to analyze the evolution trends of single cell research field.The method of extending post-discretized analysis from time dimension to space dimension is distinctive and insightful.
文摘This study investigates how universities are developing normativeframeworks to regulate the use of generative AI tools in higher education, with aparticular focus on balancing empowerment and discipline. Drawing on theoreticallenses such as Foucault’s discipline theory and contemporary AI ethics, the paperanalyzes policy documents from institutions including Harvard, Oxford, and severaltop Chinese universities. Using Latent Dirichlet Allocation (LDA) topic modeling,the study reveals five dominant governance themes—ranging from academicintegrity enforcement to pedagogical empowerment. The findings highlight a globalshift from restrictive to balanced, ethics-informed AI governance, with significantdisciplinary variations. The paper concludes by proposing a “principledpermissiveness” model that combines transparency, accountability, andpedagogical innovation in future AI governance.
基金the General Project of the National Social Science Fund,"Research on Social Anxiety Relief through New Mainstream Media"(Approval No.:23BXW068).
文摘With Chinese science fiction films and TV series gradually entering the international stage,this study focuses on English-language reviews on Internet Movie Database(IMDb)and Letterboxd.Using Latent Dirichlet Allocation(LDA)topic modeling,it analyzes the international reception of the Chinese sci-fi work The Three-Body Problem,aiming to uncover the main discussion points and differences in attention among foreign audiences for the two versions of The Three-Body Problem through text-mining techniques.The findings reveal differences between the two versions in terms of target audience,cultural presentation,adaptation evaluation,and focus in plot discussion.Moreover,this study explores new pathways for Chinese sci-fi media to go global from four dimensions:channel,content,communicator,and recipient.The study provides not only a novel perspective for understanding the international influence of Chinese sci-fi media but also empirical support for strategies in the international dissemination of Chinese cultural products.
文摘The selection and coordinated application of government innovation policies are crucial for guiding the direction of enterprise innovation and unleashing their innovation potential.However,due to the lengthy,voluminous,complex,and unstructured nature of regional innovation policy texts,traditional policy classification methods often overlook the reality that these texts cover multiple policy topics,leading to lack of objectivity.In contrast,topic mining technology can handle large-scale textual data,overcoming challenges such as the abundance of policy content and difficulty in classification.Although topic models can partition numerous policy texts into topics,they cannot analyze the interplay among policy topics and the impact of policy topic coordination on enterprise innovation in detail.Therefore,we propose a big data analysis scheme for policy coordination paths based on the latent Dirichlet allocation(LDA)model and the fuzzyset qualitative comparative analysis(fsQCA)method by combining topic models with qualitative comparative analysis.The LDA model was employed to derive the topic distribution of each document and the word distribution of each topic and enable automatic classi-fication through algorithms,providing reliable and objective textual classification results.Subsequently,the fsQCA method was used to analyze the coordination paths and dynamic characteristics.Finally,experimental analysis was conducted using innovation policy text data from 31 provincial-level administrative regions in China from 2012 to 2021 as research samples.The results suggest that the proposed method effectively partitions innovation policy topics and analyzes the policy configuration,driving enterprise innovation in different regions.
基金Evolution Trend and Coping Strategies of Online Public Opinions on Emergencies Based on Big Data Analysis(Grant No.:18CHLJ22)Social Science Planning Project in Shandong Province+1 种基金“Online Public Opinion Concerning Ethnic Factors Based on Big Data Analysis”(Grant No.:2018-GMB-022)Ethnic Research Project of State Ethnic Affairs Commission in China.
文摘The progress of IT technology such as social network and mobile payment and the change of social economic environment promote the emergence of sharing economy.As a subversive business model,the sharing economy is growing at an alarming rate all over the world.However,the influencing factors of consumers'continuous participation in the sharing economy are not clear.The paper aims to clarify the relationship between consumer perceived value and repeat purchase intention in the sharing economy.Taking the sharing economy platform(Airbnb)as an example,it proposes a dimension framework of consumer perceived value in peer-to-peer(P2P)accommodation rental service,including functional value,hedonic value,epistemic value and social relationship value.This paper used big data technology to crawl online reviews of P2P accommodation platform.LDA(Latent Dirichlet Allocation)topic model and sentiment analytics method were applied to construct the measurement indicators of perceived value based on online reviews.And repeat purchase intention variables were extracted from online reviews.Then structural equation model was used to examine the effect of perceived value dimensions on it.The paper identified that perceived value has a positive impact on consumers'repurchase intention in P2P accommodation.Also,social relationship value was considered as the most important influencing factor.
文摘The development and utilization of new and renewable resources of energy has become an important layout of the development strategy in China.Photovoltaic industry is an important strategic emerging industry for the development and utilization of new energy in China.Therefore,it is important for the government to make policy to ensure the stable and orderly development of photovoltaic enterprises to accelerate the industrial structure transition in China.This paper collects the policies on photovoltaic industry,and then analyzes the industrial policy with Latent Dirichlet Allocation(LDA).LDA is generally used in document topic label extraction and recommendation system.However,this paper applies it to policy theme analysis to study the impact of policy information flow on the risk of photovoltaic enterprises.Previous studies on photovoltaic enterprise risk examined traditional financial indicators,such as asset-liability Ratio and ROE.However,the textual information in the industrial policy has rarely been studied to quantitatively analyze photovoltaic enterprise risk.In our proposed method,LDA is first used to extract the text features hiding in the text of the industrial policies,and deep neural networks then are trained on the data,which include the text features and traditional numeric features for predict photovoltaic enterprise risk.The experimental results show that the industrial policy of the current quarter has a significant effect on photovoltaic enterprise risk.Compared with this,the industrial policy of last quarter has a weak impact on the photovoltaicenterprise risk.The proposed model is a useful tool for the prediction of the photovoltaic enterprise risk.
基金supported by Major Program of the National Social Science Foundation of China(Grant No.22&ZD139)This research was also funded by Tianjin Science and Technology Planning Project(Grant No.22ZLGCGX00060).The reviewers'comments are also highly appreciated.
文摘The quality of logistics services affects the performance and competitiveness of express logistics companies.Conventional evaluation methods of service quality are based on constructing a rating index system and then comprehensively evaluating the associated questionnaires and interviews.Such methods are often subjective,time-consuming,and include a limited sample size(data).On the other hand,customers'opinions concerning logistics services can now be deciphered using online customer reviews.Therefore,this study proposes a method combining the models of latent Dirichlet allocation and long-short term memory(LDA-LSTM)to overcome the limitations of conventional evaluation methods.The main contributions include four aspects.First,the LDA-LSTM model can extract comprehensive aspects and opinions to develop evaluation indexes and scores for logistics service quality,and the reviews of a logistics firm can be examined to verify the effectiveness of this method.Second,the LDA-LSTM model can handle a situation in which several aspects and opinions express one topic or sentiment,and it outperforms the joint sentiment topic model(JST)and naive Bayes classification(NB).Third,positive and negative ratings can reflect a firm's overall service quality,with an excellent rating highlighting the best service quality,which can provide a multi-dimensional evaluation.Fourth,we also identify the indicators of logistics service quality on which customers focus,and we compare the service quality among express enterprises in China.
文摘Revealing and comparing the evolution process of hot topics in the field of Digital Library in China and abroad.[Methods]:Taking data in the field of Digital Library from core journals in CKNI and Web of Science from 1990 s to 2020,topics are extracted by LDA model and hot topics are selected based on life cycle theory.Topic evolution paths are generated to contrast evolution of hot topics between home and abroad which are grouped into dimensions of technology and application.It fails to analyze the lagging performance and reasons of research hot topics in the field of Digital Library at home and abroad.In technological dimension of Digital Library,the research content in China lags behind that at abroad.In terms of application dimension,Chinese application tends to focus on social sciences,while application at abroad tends to focus on natural sciences.The evolution of overall research focus is U-shaped,which gradually shifted from technological research to application research,and now turn back to technological dimension.Nowadays,there are also many emerging topics combined with big data technology.
基金The Basic Research Project of Liaoning Provincial Department of Education(LJKMZ20221469)The Economic and Social Development Research Project of Liaoning Province(2024Lsbkt-104)。
文摘Tracking and investigating tourist satisfaction and accurately identifying the key factors that affect tourist satisfaction have always been among the top priorities for academia and tourist attraction operators.With the rise of online travel,analysis based on online comments has become an important method for tracking and surveying tourist satisfaction.This article examined the online comments of tourists for the Panjin Red Beach Scenic Corridor Scenic Area(hereinafter referred to as Red Beach)on Ctrip as an example.Using natural language processing to classify the tourist evaluations into topics,the main topics of concern were identified as tourism services,tourism attractions,scenic area management,and tourism experience.Through the 5-level rating of Ctrip’s online gaming customer satisfaction,an analysis was conducted on tourist satisfaction and the topics of greatest concern to the tourists were ranked.The results showed that the satisfaction levels from high to low are:tourism experience,tourism attractions,scenic area management,and tourism services.Therefore,satisfaction with related content under the service topic was the lowest so this aspect urgently needs to be improved and enhanced.
基金funded by the National Natural Science Foundation of China(Grant No.52378034)。
文摘Architectural programming is of significant importance in improving urban planning efficiency,enhancing the sustainability of the construction industry,and supporting the development of social infrastructure.However,due to its broad scope and complex content,research in this area exhibits diverse and scattered characteristics.To comprehensively understand the current status,progress,and future directions of architectural programming research,and to provide recommendations for both China and other countries,this study conducted a comparative review of relevant literature using a mixed method approach of bibliometrics,latent Dirichlet allocation(LDA)topic modeling,and systematic review.The study summarized the differences between China and other countries in six topics within the field of architectural programming:(1)intelligent and digital methods,(2)sustainability and practices,(3)project management and decision support,(4)urban planning and existing development,(5)social infrastructure and public services,and(6)architectural education and talent cultivation.This study provides an overview and comparative analysis on architectural programming,offering important reference for future research and practice,and promoting the overall development of the construction industry.
基金Supported by Suzhou Key Laboratory of Artificial Intelligence and Social Governance Technologies(SZS2023007)Smart Social Governance Technology and Innovative Application Platform(YZCXPT2023101)the Innovation System of the Integration between Industry and Education for Smart Governance(CJRH2024101)。
文摘In the modern economy,startups are not only significant drivers of innovation and technological progress but also key players in addressing employment issues and promoting economic diversification.However,startups often face substantial operational risks and uncertainties in their early stages,especially regarding financing.To uncover the impact of different resource allocations and strategic choices on financing success,this study proposes a predictive method based on the latent Dirichlet allocation(LDA)topic model and deep neural networks through an in-depth analysis of startup financing cases.We systematically collected description text data from 2,000 startups and extracted text features from these descriptions using the LDA topic model.These features,combined with several traditional numerical indicators such as industry,product type,technology type,number of employees,and company size,were used to train a deep neural network to predict startup financing outcomes.The experimental results show that the prediction performance based on the LDA topic model is significantly better than that of traditional models relying solely on numerical data.This highlights the importance of text features in predicting the success of startup financing.
文摘With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring difficulties for potential consumers to summary all the heterogenous reviews forreference. This paper proposes a new ranking method through online reviews based on differentaspects of the alternative products, which combines both objective and subjective sentiment values.Firstly, weights of these aspects are determined with LDA topic model to calculate the objectivesentiment value of the product. During this process, the realistic meaning of each aspect is alsosummarized. Then, consumers＇ personalized preferences are taken into consideration while calculatingtotal scores of alternative products. Meanwhile, comparative superiority between every two productsalso contributes to their final scores. Therefore, a directed graph model is constructed and the finalscore of each product is computed by improved PageRank algorithm. Finally, a case study is given toillustrate the feasibility and effectiveness of the proposed method. The result demonstrates that whileconsidering only objective sentiment values of the product, the ranking result obtained by our proposedmethod has a strong correlation with the actual sales orders. On the other hand, if consumers expresssubjective preferences towards a certain aspect, the final ranking is also consistent with the actualperformance of alternative products. It provides a new research idea for online customer review miningand personalized recommendation.
