基于BERTopic模型的国外信息资源管理研究进展分析

[目的/意义]文章从已有研究成果中提取主题,梳理主要研究方向,展示主题热度变化趋势,为了解和评估国外信息资源管理(IRM)研究发展现状与趋势提供参考。[方法/过程]采用新兴BERTopic模型对2013—2022年期间WoS数据库中IRM相关文献进行主题提取与识别,结合相关主题词及主题距离划分研究方向,并利用动态主题模型揭示国外IRM领域的演变过程。[结果/结论]国外IRM近10年的研究可分为59个主题,可归纳为信息技术及应用、企业信息管理、图书馆管理与服务、健康信息管理、信息用户与服务、IRM基本理论与方法、文献计量与评价7个方向。大多数主题的研究热度变化偏向稳定的趋势,数字化建设、开放数据等部分主题热度逐渐上涨,而外包、知识管理等少数主题热度退却。

基于BERTopic模型的网络暴力事件衍生舆情探测

[研究目的]在海量用户生成内容中及时探测和剖析网络暴力事件的衍生舆情能够为舆情事件链的演化分析、同类舆情的研判介入、衍生事件的监测预警提供理论支持。[研究方法]使用BERTopic模型对短文本内容主题建模并采用聚类的方式展示主题的潜在层次结构。根据词向量余弦相似度设计主题衍生度的计量算法,同时融合词共现网络在文档-词语层面信息捕捉的优势以及桑基图直观演示舆情演化过程的特点,衡量主题间的影响力与衍生关系。[研究结论]在开源数据集下多组主题模型的对照实验中,BERTopic模型在短文本建模以及下游任务的平均得分提高2.13%。在网络暴力热点事件的应用实例中,多维细粒度分析与交互式可视化方法可达到直观展示暴力事件的主题聚类、词义关联与演化态势的效果,实现网络暴力事件衍生舆情的探测与分析。

基于BERTopic的中药治疗眼科疾病的处方用药规律分析

目的分析中医药治疗眼科疾病的处方用药规律,探讨治疗眼科疾病的更多潜在配伍组合。方法收集眼科专家门诊的中医处方并整理筛选,采用Python 3.9.10中BERTopic算法对复发性虹膜睫状体炎的所有处方进行分析,得到药物关键词聚类组合;采用SPSS 25.0对中药处方数据进行层次聚类,比较两种方法的优劣和药物配伍组合的挖掘效果。结果采用BERTopic算法得到核心药物关键词组合累计2种,包括酒地龙、土茯苓、大青叶、葎草等;应用层次聚类得到的药物组合包括葎草、土茯苓、菝葜等。应用BERTopic技术的算法具有不易受噪声数据影响、提高聚类效率、增强对于处方文本语义理解等诸多优势。结论使用基于BERTopic技术的算法在寻找疾病潜在中药配伍中表现良好,潜力较大,能为眼科疾病的中药配伍组合提供更多参考方向。

A Video Captioning Method by Semantic Topic-Guided Generation

In the video captioning methods based on an encoder-decoder,limited visual features are extracted by an encoder,and a natural sentence of the video content is generated using a decoder.However,this kind ofmethod is dependent on a single video input source and few visual labels,and there is a problem with semantic alignment between video contents and generated natural sentences,which are not suitable for accurately comprehending and describing the video contents.To address this issue,this paper proposes a video captioning method by semantic topic-guided generation.First,a 3D convolutional neural network is utilized to extract the spatiotemporal features of videos during the encoding.Then,the semantic topics of video data are extracted using the visual labels retrieved from similar video data.In the decoding,a decoder is constructed by combining a novel Enhance-TopK sampling algorithm with a Generative Pre-trained Transformer-2 deep neural network,which decreases the influence of“deviation”in the semantic mapping process between videos and texts by jointly decoding a baseline and semantic topics of video contents.During this process,the designed Enhance-TopK sampling algorithm can alleviate a long-tail problem by dynamically adjusting the probability distribution of the predicted words.Finally,the experiments are conducted on two publicly used Microsoft Research Video Description andMicrosoft Research-Video to Text datasets.The experimental results demonstrate that the proposed method outperforms several state-of-art approaches.Specifically,the performance indicators Bilingual Evaluation Understudy,Metric for Evaluation of Translation with Explicit Ordering,Recall Oriented Understudy for Gisting Evaluation-longest common subsequence,and Consensus-based Image Description Evaluation of the proposed method are improved by 1.2%,0.1%,0.3%,and 2.4% on the Microsoft Research Video Description dataset,and 0.1%,1.0%,0.1%,and 2.8% on the Microsoft Research-Video to Text dataset,respectively,compared with the existing video captioning methods.As a result,the proposed method can generate video captioning that is more closely aligned with human natural language expression habits.

基于BERTopic模型的数字政府治理领域的主题识别与内容分析

[目的/意义]在信息技术创新、政府数字化转型的背景下,中国学术界输出的“数字政府治理”相关研究体量较大,现阶段亟待对其进行科学整理和综述概览,以回应“政府治理现代化”目标对理论研究的新要求。[方法/过程]以2011-2023年间中国知网数据库收录的有关数字政府治理研究的1829篇文献为数据来源,借助深度学习模型BERTopic和文献计量法CiteSpace相互验证分析数字政府治理领域的研究阶段、研究关键词、研究热点主题及内容等,使得结果具有高准确性和强解释性,以科学有效地探测主题取向及特征,展望数字政府治理领域的未来研究方向。[结果/结论]中国数字政府治理研究在过去的10年间发展迅猛,已成为学者探索的热点论域;现有文献研究热点整体上聚焦于价值导向、治理模式进化、技术工具嵌入;重点主题关注的是“政府数据开放共享研究”“基于数字技术的政务服务改革研究”“城乡场域下的数字化治理研究”“数字政府治理水平的评估研究”及“数字化转型下具体治理领域的实践研究”。未来,数字政府治理仍然是一个具有持续性拓展空间的研究领域,将逐步实现数字政府治理的更广场域触达、精准滴灌、多项学科融合和技术实用主义。

基于BERTopic模型的组织成员工作投入研究的主题提取

和发表年份,经过文本预处理,使用BERTopic模型进行主题提取和可视化分析。[结果/结论]国内现有的关于组织成员工作投入的研究可以分为研究内容和研究方法两大主题集群,均表现出多样化态势。主题时序演化分析揭示了组织成员工作投入研究正逐步转向对个体差异和心理健康的关注。未来研究可从新技术的影响、工作投入动态变化以及跨学科合作等方面进行拓展。

基于BERTopic模型的公众心理应激信息表征分析

[目的/意义]在后疫情时代的背景下,探讨心理应激的信息表征对公众的心理健康建设及社会发展具有重要意义。[方法/过程]使用BERTopic模型对问答数据进行匹配和热度分析,探究不同主题下用户的关注内容和关注程度。[结果/结论]用户心理应激信息表征主要集中在生理反应、认知反应、情感反应、行为反应4种类型。本研究从后疫情时代的视角出发探讨心理应激的信息表征,能够以常态化心理应激干预为目标为决策者提出长期心理支持服务建议,推进线上线下并举的心理服务。

ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers

Environmental,social,and governance(ESG)factors are critical in achieving sustainability in business management and are used as values aiming to enhance corporate value.Recently,non-financial indicators have been considered as important for the actual valuation of corporations,thus analyzing natural language data related to ESG is essential.Several previous studies limited their focus to specific countries or have not used big data.Past methodologies are insufficient for obtaining potential insights into the best practices to leverage ESG.To address this problem,in this study,the authors used data from two platforms:LexisNexis,a platform that provides media monitoring,and Web of Science,a platform that provides scientific papers.These big data were analyzed by topic modeling.Topic modeling can derive hidden semantic structures within the text.Through this process,it is possible to collect information on public and academic sentiment.The authors explored data from a text-mining perspective using bidirectional encoder representations from transformers topic(BERTopic)—a state-of-the-art topic-modeling technique.In addition,changes in subject patterns over time were considered using dynamic topic modeling.As a result,concepts proposed in an international organization such as the United Nations(UN)have been discussed in academia,and the media have formed a variety of agendas.

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。

TG-SMR:AText Summarization Algorithm Based on Topic and Graph Models

Recently,automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization.However,most of the computing methods that are used in real systems are based on graph models,which are characterized by their simplicity and stability.Thus,this paper proposes an improved extractive text summarization algorithm based on both topic and graph models.The methodology of this work consists of two stages.First,the well-known TextRank algorithm is analyzed and its shortcomings are investigated.Then,an improved method is proposed with a new computational model of sentence weights.The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods.Finally,through experiments on the DUC2004 and DUC2006 datasets,our proposed improved graph model algorithm TG-SMR(Topic Graph-Summarizer)is compared to other text summarization systems.The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores.It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.

Water-responsive gel extends drug retention and facilitates skin penetration for curcumin topical delivery against psoriasis

Psoriasis is a chronic inflammatory skin disease characterized by erythema,scaling,and skin thickening.Topical drug application is recommended as the first-line treatment.Many formulation strategies have been developed and explored for enhanced topical psoriasis treatment.However,these preparations usually have low viscosity and limited retention on the skin surface,resulting in low drug delivery efficiency and poor patient satisfaction.In this study,we developed the first water-responsive gel(WRG),which has a distinct water-triggered liquid-to-gel phase transition property.Specifically,WRG was kept in a solution state in the absence of water,and the addition of water induced an immediate phase transition and resulted in a high viscosity gel.Curcumin was used as a model drug to investigate the potential of WRG in topical drug delivery against psoriasis.In vitro and in vivo data showed that WRG formulation could not only extend skin retention but also facilitate the drug permeating across the skin.In a mouse model of psoriasis,curcumin loaded WRG(CUR-WRG)effectively ameliorated the symptoms of psoriasis and exerted a potent anti-psoriasis effect by extending drug retention and facilitating drug penetration.Further mechanism study demonstrated that the anti-hyperplasia,anti-inflammation,anti-angiogenesis,anti-oxidation,and immunomodulation properties of curcumin were amplified by enhanced topical drug delivery efficiency.Notably,neglectable local or systemic toxicity was observed for CUR-WRG application.This study suggests that WRG is a promising formulation for topically psoriasis treatment.

Topic Controlled Steganography via Graph-to-Text Generation

Generation-based linguistic steganography is a popular research area of information hiding.The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to.However,in the course of our experiment,we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text,which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases,and that the topic of generated texts is uncontrollable,so there is still room for improvement in concealment.In this paper,we propose a topic-controlled steganography method which is guided by graph-to-text generation.The proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs,and the topic of the generated texts is controllable.We also provide a graph path coding method with corresponding detailed algorithms for graph-to-text generation.Different from traditional linguistic steganography methods,we encode the secret information during graph path coding rather than using conditional probability.We test our method in different aspects and compare it with other text generative steganographic methods.The experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text.

Research on high-performance English translation based on topic model

Retelling extraction is an important branch of Natural Language Processing(NLP),and high-quality retelling resources are very helpful to improve the performance of machine translation.However,traditional methods based on the bilingual parallel corpus often ignore the document background in the process of retelling acquisition and application.In order to solve this problem,we introduce topic model information into the translation mode and propose a topic-based statistical machine translation method to improve the translation performance.In this method,Probabilistic Latent Semantic Analysis(PLSA)is used to obtains the co-occurrence relationship between words and documents by the hybrid matrix decomposition.Then we design a decoder to simplify the decoding process.Experiments show that the proposed method can effectively improve the accuracy of translation.

Smart object recommendation based on topic learning and joint features in the social internet of things

With the extensive integration of the Internet,social networks and the internet of things,the social internet of things has increasingly become a significant research issue.In the social internet of things application scenario,one of the greatest challenges is how to accurately recommend or match smart objects for users with massive resources.Although a variety of recommendation algorithms have been employed in this field,they ignore the massive text resources in the social internet of things,which can effectively improve the effect of recommendation.In this paper,a smart object recommendation approach named object recommendation based on topic learning and joint features is proposed.The proposed approach extracts and calculates topics and service relevant features of texts related to smart objects and introduces the“thing-thing”relationship information in the internet of things to improve the effect of recommendation.Experiments show that the proposed approach enables higher accuracy compared to the existing recommendation methods.

Topic Modelling and Sentimental Analysis of Students’Reviews

Globally,educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 pandemic.The fundamental concern has been the continuance of education.As a result,several novel solutions have been developed to address technical and pedagogical issues.However,these were not the only difficulties that students faced.The implemented solutions involved the operation of the educational process with less regard for students’changing circumstances,which obliged them to study from home.Students should be asked to provide a full list of their concerns.As a result,student reflections,including those from Saudi Arabia,have been analysed to identify obstacles encountered during the COVID-19 pandemic.However,most of the analyses relied on closed-ended questions,which limited student involvement.To delve into students’responses,this study used open-ended questions,a qualitative method(content analysis),a quantitative method(topic modelling),and a sentimental analysis.This study also looked at students’emotional states during and after the COVID-19 pandemic.In terms of determining trends in students’input,the results showed that quantitative and qualitative methods produced similar outcomes.Students had unfavourable sentiments about studying during COVID-19 and positive sentiments about the face-to-face study.Furthermore,topic modelling has revealed that the majority of difficulties are more related to the environment(home)and social life.Students were less accepting of online learning.As a result,it is possible to conclude that face-to-face study still attracts students and provides benefits that online study cannot,such as social interaction and effective eye-to-eye communication.

Topic-Aware Abstractive Summarization Based on Heterogeneous Graph Attention Networks for Chinese Complaint Reports

Automatic text summarization(ATS)plays a significant role in Natural Language Processing(NLP).Abstractive summarization produces summaries by identifying and compressing the most important information in a document.However,there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics.In particular,Chinese complaint reports,generated by urban complainers and collected by government employees,describe existing resident problems in daily life.Meanwhile,the reflected problems are required to respond speedily.Therefore,automatic summarization tasks for these reports have been developed.However,similar to traditional summarization models,the generated summaries still exist problems of informativeness and conciseness.To address these issues and generate suitably informative and less redundant summaries,a topic-based abstractive summarization method is proposed to obtain global and local features.Additionally,a heterogeneous graph of the original document is constructed using word-level and topic-level features.Experiments and analyses on public review datasets(Yelp and Amazon)and our constructed dataset(Chinese complaint reports)show that the proposed framework effectively improves the performance of the abstractive summarization model for Chinese complaint reports.

Usage of topical insulin for the treatment of diabetic keratopathy,including corneal epithelial defects

BACKGROUND Diabetic keratopathy(DK)occurs in 46%-64%of patients with diabetes and requires serious attention.In patients with diabetes,the healing of corneal epithelial defects or ulcers takes longer than in patients without diabetes.Insulin is an effective factor in wound healing.The ability of systemic insulin to rapidly heal burn wounds has been reported for nearly a century,but only a few studies have been performed on the effects of topical insulin(TI)on the eye.Treatment with TI is effective in treating DK.AIM To review clinical and experimental animal studies providing evidence for the efficacy of TI to heal corneal wounds.METHODS National and international databases,including PubMed and Scopus,were searched using relevant keywords,and additional manual searches were conducted to assess the effectiveness of TI application on corneal wound healing.Journal articles published from January 1,2000 to December 1,2022 were examined.The relevancy of the identified citations was checked against predetermined eligibility standards,and relevant articles were extracted and reviewed.RESULTS A total of eight articles were found relevant to be discussed in this review,including four animal studies and four clinical studies.According to the studies conducted,TI is effective for corneal re-epithelialization in patients with diabetes based on corneal wound size and healing rate.CONCLUSION Available animal and clinical studies have shown that TI promotes corneal wound healing by several mechanisms.The use of TI was not associated with adverse effects in any of the published cases.Further studies are needed to enhance our knowledge and understanding of TI in the healing of DK.

Research on the Chinese Path to Modernization in History,Key Topics,and Outlook:A Cite Space-based Bibliometric Analysis

In the speech delivered at the centenary celebration of the Communist Party of China(CPC),Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,made important remarks on the“Chinese path to modernization.”This term represents the latest achievement China has scored in adapting Marxism to the Chinese context and the needs of the times.It has set the course as China embarks on a new journey of building a strong socialist country with Chinese characteristics and achieving national rejuvenation.Drawing on CiteSpace,we conducted a visualized bibliometric analysis of literature on the Chinese path to modernization by searching the CNKI database using subject terms such as“Chinese modernization,Chinese path to modernization,and Chinese-style modernization.”The findings reveal that:(a)Research on the Chinese path to modernization has gone through three stages:initial establishment,pioneering exploration,and comprehensive in-depth development.(b)Existing literature has covered the four key topics associated with the Chinese path to modernization,namely its essence,goal,methodology,and pioneering achievements.(c)Future research may focus on building up China’s strength in agriculture,developing the digital economy,modernizing China’s system and capacity for governance,and establishing a unique socialist discourse system for Chinese modernization.

Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling

The COVID-19 pandemic has become one of the severe diseases in recent years.As it majorly affects the common livelihood of people across the universe,it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak.The public opinions are been shared enormously in microblogging med-ia like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics,sports,entertainment etc.,This work presents a combination of Intensity Based Emotion Classification Convolution Neural Net-work(IBEC-CNN)model and Non-negative Matrix Factorization(NMF)for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those tweets.The topics were identified using NMF and the emotions are classified using pretrained IBEC-CNN,based on predefined intensity scores.The research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 pandemic.Using the Twitter Application Programming Interface(Twitter API),huge numbers of COVID-19 tweets are retrieved during January and July 2020.The extracted tweets are ana-lyzed for emotions fear,joy,sadness and trust with proposed Intensity Based Emotion Classification Convolution Neural Network(IBEC-CNN)model which is pretrained.The classified tweets are given an intensity score varies from 1 to 3,with 1 being low intensity for the emotion,2 being the moderate and 3 being the high intensity.To identify the topics in the tweets and the themes of those topics,Non-negative Matrix Factorization(NMF)has been employed.Analysis of emotions of COVID-19 tweets has identified,that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%.Also,more than 75%nega-tive tweets expressed sadness,fear are of low intensity.A qualitative analysis has also been conducted and the topics detected are grouped into themes such as eco-nomic impacts,case reports,treatments,entertainment and vaccination.The results of analysis show that the issues related to the pandemic are expressed dif-ferent emotions in twitter which helps in interpreting the public insights during the pandemic and these results are beneficial for planning the dissemination of factual health statistics to build the trust of the people.The performance comparison shows that the proposed IBEC-CNN model outperforms the conventional models and achieved 83.71%accuracy.The%of COVID-19 tweets that discussed the different topics vary from 7.45%to 26.43%on topics economy,Statistics on cases,Government/Politics,Entertainment,Lockdown,Treatments and Virtual Events.The least number of tweets discussed on politics/government on the other hand the tweets discussed most about treatments.

Effectiveness of topical oxygen therapy in wound healing for patients with diabetic foot ulcer

Objectives:Non-healing wounds have been one of the major challenges in health care because of increased morbidity,especially for those who have diabetes mellitus.Numerous regimens are being innovated to produce an evidence-based practice that would minimize complications and promote healing.Topical oxygen therapy is an innovation in wound care that has been considered influential in the wound healing process.This intervention aims to increase the oxygen concentration in the affected limb to promote wound healing.Methods:This research applied an experimental design that targeted a total of 60 adult patients aged 45–64 years with diabetic foot ulcers.A randomized systematic sampling technique was used to allow equal chances and prevent bias.In total,30 patients in the control group received usual care for diabetic foot ulcers,and the remaining 30 patients in the experimental group received topical oxygen therapy together with standard care for diabetic foot ulcers.Subjects were assessed using the Wagner-Meggitt Wound Classification System.Results:The result proved that there was a significant difference in the wound grade of patients in the experimental group after the application of the usual wound care plus the topical oxygen therapy using Friedman's test.The control and experimental groups were compared using Mann–Whitney statistical analyses,and the results showed that there was a significant difference between the control and experimental groups after the application of topical oxygen therapy.Conclusions:Topical oxygen therapy was demonstrated to be effective to aid in the wound healing process of patients with diabetic foot ulcers.Fur ther research was recommended to improve the application of topical oxygen therapy to patients with chronic wounds and promote the wound healing process.