Three decades of topic evolution,hot spot mining and prospect in CCUS Studies based on CitNetExplorer

As a major strategic technology for reducing greenhouse gas emissions and ensuring energy security,carbon capture,utilization,and storage(CCUS)is of great significance to large-scale emission reduction.From the perspective of knowledge discovery,it is important to analyse the study progress based on existing study achievements,excavate the evolution characteristics of study topics over time,review stage-specific findings,and construct CCUS domain knowledge map.This will help researchers gain an overall understanding of CCUS studies and promote the industry-college-research cooperation in respect to CCUS.Based on the Web of Science(WOS)database platform and CitNet-Explorer software,the present study explore the international research progress,topic evolution track,research hotspot and research trend of CCUS technology since its birth nearly 30 years ago,using bibliometric method,citation network visualization analysis method and cluster analysis method.Through the analysis of literature citation network,it is found that:16 CCUS topics,6 hotspots have been studied in the last three decades.The topics of CCUS studies present an evolution path from CCUS technology security and economicfeasibility analysis to CCUS technological popularization,and then CCUS technological improvement and development.Cutting-edge CCUS looks at the process and infrastructure construction,cost effectiveness and development prospect analysis.CCUS focuses on improvement of process technologies and related infrastructure.

Design and Implementation of On-Line Hot Topic Discovery Model

Internet has become a major medium for infomation transmission, how to detect hot topic on web, track the event development and forecast emergency is important to many fields, particularly to some government departments. On the basis of the researches in the field of topic detection and tracking, we propose a model for hot topic discovery that will pick out hot topics by automatically detecting, clustering and weighting topics on the websites within a time period. Based on the idea of stock index, we also introduce a topic index approach in following the growth of topics, which is useful to analyze and forecast the development of topics on web.

Topic Evolution and Emerging Topic Analysis Based on Open Source Software

Purpose:We present an analytical,open source and flexible natural language processing and text mining method for topic evolution,emerging topic detection and research trend forecasting for all kinds of data-tagged text.Design/methodology/approach:We make full use of the functions provided by the open source VOSviewer and Microsoft Office,including a thesaurus for data clean-up and a LOOKUP function for comparative analysis.Findings:Through application and verification in the domain of perovskite solar cells research,this method proves to be effective.Research limitations:A certain amount of manual data processing and a specific research domain background are required for better,more illustrative analysis results.Adequate time for analysis is also necessary.Practical implications:We try to set up an easy,useful,and flexible interdisciplinary text analyzing procedure for researchers,especially those without solid computer programming skills or who cannot easily access complex software.This procedure can also serve as a wonderful example for teaching information literacy.Originality/value:This text analysis approach has not been reported before.

Detecting Dynamics of Hot Topics with Alluvial Diagrams:A Timeline Visualization

Purpose： In this paper, we combined the method of co-word analysis and alluvial diagram to detect hot topics and illustrate their dynamics. Design/methodology/approach： Articles in the field of scientometrics were chosen as research cases in this study. A time-sliced co-word network was generated and then clustered. Afterwards, we generated an alluvial diagram to show dynamic changes of hot topics, including their merges and splits over time. Findings： After analyzing the dynamic changes in the field of scientometrics from 2011 to 2015, we found that two clusters being merged did not mean that the old topics had disappeared and a totally new one had emerged. The topics were possibly still active the following year, but the newer topics had drawn more attention. The changes of hot topics reflected the shift in researchers＇ interests. subdivided and re-merged. For example, several topics as research progressed. Research topics in scientometrics were constantly a cluster involving ＂industry＂ was divided into Research limitations： When examining longer time periods, we encounter the problem of dealing with bigger data sets. Analyzing data year by year would be tedious, but if we combine, e.g. two years into one time slice, important details would be missed. Practical implications： This method can be applied to any research field to illustrate the dynamics of hot topics. It can indicate the promising directions for researchers and provide guidance to decision makers. Originality/value： The use of alluvial diagrams is a distinctive and meaningful approach to detecting hot topics and especially to illustrating their dynamics.

A Metric Approach to Hot Topics in Biomedicine via Keyword Co-occurrence

Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic biology.Design/methodology/approach:We set up their keyword co-occurrence networks with using three indicators and information visualization for metric analysis.Findings:The results reveal the main research hotspots in the three topics are different,but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each other.Research limitations:All analyses use keywords,without any other forms.Practical implications:We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions,and for promoting biomedical developments.Originality/value:We chose the core keywords in three research hot topics in biomedicine by using h-index.

ICS-SVM:A user retweet prediction method for hot topics based on improved SVM

In social networks,many complex factors affect the prediction of user forwarding behavior.This paper proposes an improved SVM prediction method for user forwarding behavior of hot topics to improve prediction accuracy.Firstly,we consider that the improved Cuckoo Search algorithm can select the optimal penalty parameters and kernel function parameters to optimize the SVM and thus predict the user's forwarding behavior.Secondly,this paper considers the factors that affect the user forwarding behavior comprehensively from the user's own factors and external factors.Finally,based on the characteristics of the user's forwarding behavior changing over time,the time-slicing method is used to predict the trend of hot topics.Experiments show that the method can accurately predict the user's forwarding behavior and can sense the trend of hot topics.

Predicting the Hot Topics with User Sentiments

Social applications such as Weibo have provided a quick platform for information propagation, which have led to an explosive propagation for hot topic. User sentiments about propagation information play an important role in propagation speed, which receive more and more attention from data mining field. In this paper, we propose an sentiment-based hot topics prediction model called PHT-US. PHT-US firstly classifies a large amount of text data in Weibo into different topics, then converts user sentiments and time factors into embedding vectors that are input into recurrent neural networks (both LSTM and GRU), and predicts whether the target topic could be a hot spot. Experiments on Sina Weibo show that PHT-US can effectively predict the hot topics in the future. Social applications such as Weibo provide a platform for quick information propagation, which leads to an explosive propagation for hot topics. User sentiments about propagation information play an important role in propagation speed, and thus receive more attention from data mining field. In this paper, a sentiment-based hot topics prediction model called PHT-US is proposed. Firstly a large amount of text data in Weibo was classified into different topics, and then user sentiments and time factors were converted into embedding vectors that are input into recurrent neural networks (both LSTM and GRU), and future hotspots were predicted. Experiments on Sina Weibo show that PHT-US can effectively predict hot topics in the future.

Contrastive analysis in China and abroad on the Evolution of hot topics in the field of digital library based on LDA model

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.

Topic evolution based on the probabilistic topic model： a review

Accurately representing the quantity and characteristics of users＇ interest in certain topics is an important problem facing topic evolution researchers, particularly as it applies to modem online environments. Search engines can provide information retrieval for a specified topic from archived data, but fail to reflect changes in interest toward the topic over time in a structured way. This paper reviews notable research on topic evolution based on the probabilistic topic model from multiple aspects over the past decade. First, we introduce notations, terminology, and the basic topic model explored in the survey, then we summarize three categories of topic evolution based on the probabilistic topic model： the discrete time topic evolution model, the continuous time topic evolution model, and the online topic evolution model. Next, we describe applications of the topic evolution model and attempt to summarize model generalization performance evaluation and topic evolution evaluation methods, as well as providing comparative experimental results for different models. To conclude the review, we pose some open questions and discuss possible future research directions.

Mapping the evolution of research topics using ATM and SNA

Purpose:This paper introduces an analysis framework for tracking the evolution of research topics at the selected topics level,covering a research topic’s evolution trend,evolution path and its content changes over time.Design/methodology/approach:After the topics were recovered by the author-topic model,we first built the keyword-topic co-occurrence network to track the dynamics of topic trends.Then a single-mode network was constructed with each node representing a topic and edge indicating the relationship between topics.It was used to illustrate the evolution path and content changes of research topics.A case study was conducted on the digital library research in China to verify the effectiveness of the analysis framework.Findings:The experimental results show that this analysis framework can be used to track evolution of research topics at a micro level and using social network analysis method can help understand research topics’evolution paths and content changes with the passage of time.Research limitations:Using the analysis framework will produce limited results when examining unstructured data such as social media data.In addition,the effectiveness of the framework introduced in this paper needs to be verified with more research topics in information science and in more scientific fields.Practical implications:This analysis framework can help scholars and researchers map research topics’evolution process and gain insights into how a field’s topics have evolved over time.Originality/value:Tbe analysis framework used in this study can help reveal more micro evolution details.The index to measure topic association strength defined in this paper reflects both similarity and dissimilarity between topics,which belps better understand research topics’evolution paths and content changes.

Approach to extracting hot topics based on network traffic content

This article presents the formal definition and description of popular topics on the Internet,analyzes the relationship between popular words and topics,and finally introduces a method that uses statistics and correlation of the popular words in traffic content and network flow characteristics as input for extracting popular topics on the Internet.Based on this,this article adapts a clustering algorithm to extract popular topics and gives formalized results.The test results show that this method has an accuracy of 16.7%in extracting popular topics on the Internet.Compared with web mining and topic detection and tracking(TDT),it can provide a more suitable data source for effective recovery of Internet public opinions.

Technology Innovation Management:Topic Evolutions and Research Trends from 1968 to 2022

The technology innovation management(TIM)field attracts an increasing amount of attention.This paper takes a retrospective look at high-quality publication output in the TIM field over the 55 years from 1968 to 2022,revealing topics,their evolutions,and research trends.A total of 31,498 articles and proceeding papers published during this period are analyzed.The paper first extracts the fine-grained topic words using the tool ITGInsight.Then Linlog algorithm is used to cluster topics based on the cooccurrence of the topic words.Time is integrated within the topic cluster results so that topic evolutions and research trends are analyzed.The TIM field has four main topic clusters:technology research,product research,firm research,and future research.In every topic cluster,there are many fine-sorted macro-topics and micro-topics.There is an obvious increase in diversity in the topic clusters of technology research and firm research.Especially,the evolution of technology research has been closely connected with society.In contrast,product research has declined in its topic size.At the same time,future research maintains a certain stability of its scientific publications.The research predicts that all the four topics will retain their popularity,and play an important role in the TIM field.Among them,technology research will continue to expand and enrich the TIM field.The other three topics will deepen their research for a better development of the TIM field.The paper also proposes some advice for industry professionals,policymakers,and researchers.

Hot topics of autoimmune encephalitis

INTRODUCTION Within the last few years,a new group of diseases featured with cognitive impairment, seizures and behavior disorders was reported. And these diseases were commonly diagnosed as 'viral encephalitis' and'sporadic encephalitis' than autoimmune encephalitis (AE) before AE had confirmed etiological.

基于Topic Model的我国档案学主题结构与演化研究

文本内容分析能够有效揭示学科研究的主题结构与知识的发展过程。本文运用主题模型与时间序列分析等方法,以档案学领域的两种CSSCI源刊近10年刊载的论文为分析对象进行文本内容挖掘。分析结果表明,上述方法的结合能够有效识别学科领域研究的主题,并揭示学科主题的发展过程;中国档案学领域近10年的研究主要集中在学科范式研究、电子文件管理、档案信息服务等12个研究主题;通过对不同主题的时间分布分析,揭示了这些主题的演化过程,进一步归纳总结了相关方法使用的主要注意事项并给出了对应建议。

Self-Adaptive Topic Model： A Solution to the Problem of ＂Rich Topics Get Richer＂

The problem of "rich topics get richer"(RTGR) is popular to the topic models,which will bring the wrong topic distribution if the distributing process has not been intervened.In standard LDA(Latent Dirichlet Allocation) model,each word in all the documents has the same statistical ability.In fact,the words have different impact towards different topics.Under the guidance of this thought,we extend ILDA(Infinite LDA) by considering the bias role of words to divide the topics.We propose a self-adaptive topic model to overcome the RTGR problem specifically.The model proposed in this paper is adapted to three questions:(1) the topic number is changeable with the collection of the documents,which is suitable for the dynamic data;(2) the words have discriminating attributes to topic distribution;(3) a selfadaptive method is used to realize the automatic re-sampling.To verify our model,we design a topic evolution analysis system which can realize the following functions:the topic classification in each cycle,the topic correlation in the adjacent cycles and the strength calculation of the sub topics in the order.The experiment both on NIPS corpus and our self-built news collections showed that the system could meet the given demand,the result was feasible.

基于BERTopic模型的用户层次化需求及动机分析--以抖音平台为例

[研究目的]在分析短视频平台的用户生成内容构成,提炼其在时间演化与社会事件影响下表现出的构造与演化规律,挖掘短视频用户的内在行为需要,探讨其用户参与行为的潜在动机因素。[研究方法]以抖音平台237万条短视频发布数据作为研究样本,使用BERTopic模型实现主题聚类,总结用户一定时间内的话题的关注情况,并在互联网视角下结合马斯洛需求层次理论,揭示用户参与行为背后需求与动机。[研究结论]首先,用户的需求关注度由高至低的排列顺序为尊重需求、安全需求、社交需求、自我实现需求与生理需求,且该关注顺序能在日常的时间推移中保持稳定;其次,用户对于社会事件有着较高的讨论度,相关事件能够显著影响时段内用户的视频内容构成,但对用户的关注程度分布影响微弱;最后,用户在发布视频过程中和点赞互动的关注热点存在差异。用户在发布视频时更关注尊重层次需求,而在浏览互动时,自我实现层次需求受到的关注程度显著提升。

摄影测量与遥感领域主题演化研究——以《ISPRS Journal of Photogrammetry and Remote Sensing》期刊为例

为了便于相关领域的研究人员了解遥感学领域的整体发展状态并进行量化分析,本文以国际摄影测量与遥感学会(ISPRS)官方刊物作为研究数据,从主题挖掘和主题关联的角度出发,在计量分析的基础上,基于隐含狄利克雷分布(LDA)和word2vec的主题演化模型进行分析与研究,重点解决了摄影测量与遥感学领域主题挖掘、学科领域主题关联、学科主题演化建模和主题演化知识图谱等问题,展示了遥感学领域的主题动态演化过程。主题演化研究结果表明:1)主题强度总排名前三的主题分别是激光扫描技术、摄影测量、遥感信息提取与分类;2)随着深度学习的深入发展,遥感学领域的深度学习相关应用研究热度逐渐攀升;3)环境遥感相关方向的研究热度相对稳定,演化关系多局限于相关研究内部。

Hotshots of Spatio-temporal Behavior of Chinese Residents in the Context of Big Data:Visual Analysis Based on CiteSpace

By using CiteSpace software to create a knowledge map of authors,institutions and keywords,the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline published in the China Academic Network Publishing Database(CNKI)was analyzed and discussed.It is found that there was a lack of communication and cooperation among research institutions and scholars;the research hotspots involved four main areas,including“application in tourism research”,“application in traffic travel research”,“application in work-housing relationship research”,and“application in personal family life research”.