A Critical Discourse Analysis of Cyberbullying Language Based on Text-mining Techniques——A Case Study of Prince Harry and Meghan Markle

With the development of AI,a large amount of cyberbullying language flooded into the Internet.Cyberbullying language damages the online environment and causes mental harm to the victims of online bullying.Many researchers,therefore,have paid much attention to the problem.This study collected online comments targeted at Prince Harry and Meghan Markle as a corpus and then analyzed text data based on Critical Discourse Analysis by using text-mining tools to explore the factors that contribute to the social ideological effects of the cyberbullying language.The research results show that cultural differences,prejudice,or social exclusion due to race or gender form cyberbullying on social media.

Topic prevalence and trends of metaverse in healthcare:a bibliometric analysis

Metaverse technology is an advanced form of virtual reality and augmented technologies. It merges the digital world with the real world, thus benefitting healthcare services. Medical informatics is promising in the metaverse. Despite the increasing adoption of the metaverse in commercial applications, a considerable research gap remains in the academic domain, which hinders the comprehensive delineation of research prospects for the metaverse in healthcare. This study employs text-mining methods to investigate the prevalence and trends of the metaverse in healthcare;in particular, more than 34,000 academic articles and news reports are analyzed. Subsequently, the topic prevalence, similarity, and correlation are measured using topic-modeling methods. Based on bibliometric analysis, this study proposes a theoretical framework from the perspectives of knowledge, socialization, digitization, and intelligence. This study provides insights into its application in healthcare via an extensive literature review. The key to promoting the metaverse in healthcare is to perform technological upgrades in computer science, telecommunications, healthcare services, and computational biology. Digitization, virtualization, and hyperconnectivity technologies are crucial in advancing healthcare systems. Realizing their full potential necessitates collective support and concerted effort toward the transformation of relevant service providers, the establishment of a digital economy value system, and the reshaping of social governance and health concepts. The results elucidate the current state of research and offer guidance for the advancement of the metaverse in healthcare.

Research on the Public Perception of Yangzhou Historical and Cultural Districts Based on Network Review Text Analysis

The historical and cultural districts of a city serve as important cultural heritage and tourism resources.This paper focused on four such districts in Yangzhou and performed semantic analysis on online public comments using ROST CM6 software.According to the high frequency words,attention preference of district site elements,activities and feelings in Yangzhou historical and cultural districts were analyzed.Through the analysis of semantic network and public emotional tendency,the relationship between the protection and utilization of Yangzhou historical and cultural districts and the perception and demand of users were discussed,and some suggestions for the protection,utilization and renewal of historical and cultural districts were put forward.

Temperature-Triggered Hardware Trojan Based Algebraic Fault Analysis of SKINNY-64-64 Lightweight Block Cipher

SKINNY-64-64 is a lightweight block cipher with a 64-bit block length and key length,and it is mainly used on the Internet of Things(IoT).Currently,faults can be injected into cryptographic devices by attackers in a variety of ways,but it is still difficult to achieve a precisely located fault attacks at a low cost,whereas a Hardware Trojan(HT)can realize this.Temperature,as a physical quantity incidental to the operation of a cryptographic device,is easily overlooked.In this paper,a temperature-triggered HT(THT)is designed,which,when activated,causes a specific bit of the intermediate state of the SKINNY-64-64 to be flipped.Further,in this paper,a THT-based algebraic fault analysis(THT-AFA)method is proposed.To demonstrate the effectiveness of the method,experiments on algebraic fault analysis(AFA)and THT-AFA have been carried out on SKINNY-64-64.In the THT-AFA for SKINNY-64-64,it is only required to activate the THT 3 times to obtain the master key with a 100%success rate,and the average time for the attack is 64.57 s.However,when performing AFA on this cipher,we provide a relation-ship between the number of different faults and the residual entropy of the key.In comparison,our proposed THT-AFA method has better performance in terms of attack efficiency.To the best of our knowledge,this is the first HT attack on SKINNY-64-64.

Text Sentiment Analysis Using Frequency-Based Vigorous Features

Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people(opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment Analysis and Prediction(SAP) solution of Text Trend through K-Nearest Neighbor(KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI(Hello World) based text analysis framework has been designed to perform the text analytics.

A Comparative Analysis of the New -3(-n) - 1 Remer Conjecture and a Proof of the 3n + 1 Collatz Conjecture

This scientific paper is a comparative analysis of two mathematical conjectures. The newly proposed -3(-n) - 1 Remer conjecture and how it is related to and a proof of the more well known 3n + 1 Collatz conjecture. An overview of both conjectures and their respective iterative processes will be presented. Showcasing their unique properties and behavior to each other. Through a detailed comparison, we highlight the similarities and differences between these two conjectures and discuss their significance in the field of mathematics. And how they prove each other to be true.

Text Analysis-Based Watermarking Approach for Tampering Detection of English Text

Due to the rapid increase in the exchange of text information via internet networks,the security and the reliability of digital content have become a major research issue.The main challenges faced by researchers are authentication,integrity verication,and tampering detection of the digital contents.In this paper,text zero-watermarking and text feature-based approach is proposed to improve the tampering detection accuracy of English text contents.The proposed approach embeds and detects the watermark logically without altering the original English text document.Based on hidden Markov model(HMM),the fourth level order of the word mechanism is used to analyze the contents of the given English text to nd the interrelationship between the contexts.The extracted features are used as watermark information and integrated with digital zero-watermarking techniques.To detect eventual tampering,the proposed approach has been implemented and validated with attacked English text.Experiments were performed using four standard datasets of varying lengths under multiple random locations of insertion,reorder,and deletion attacks.The experimental and simulation results prove the tampering detection accuracy of our method against all kinds of tampering attacks.Comparison results show that our proposed approach outperforms all the other baseline approaches in terms of tampering detection accuracy.

Interdiscursivity and Promotional Discourse:A Corpus-Assisted Genre Analysis of About Us Texts on Chinese University Websites

The debate on the marketization of discourse in higher education has sparked and sustained interest among researchers in discourse and education studies across a diversity of contexts.While most research in this line has focused on marketized discourses such as advertisements,little attention has been paid to promotional discourse in public institutions such as the About us texts on Chinese university websites.The goal of the present study is twofold:first,to describe the generic features of the university About us texts in China;and second,to analyze how promotional discourse is interdiscursively incorporated in the discourse by referring to the broader sociopolitical context.Findings have indicated five main moves:giving an overview,stressing historical status,displaying strengths,pledging political and ideological allegiance,and communicating goals and visions.Move 3,displaying strengths,has the greatest amount of information and can be further divided into six sub-moves which presents information on campus facilities,faculty team,talent cultivation,disciplinary fields construction,academic research,and international exchange.The main linguistic and rhetorical strategies used in these moves are analyzed and discussed.

Inheritance and Development of Three Pre-Qin Classics of Confucianism——An Application of Topic Modeling in Classical Chinese Text Analysis

The Analects, Mengzi and Xunzi are the top-three classical works of pre-Qin Confucianism, which epitomized thoughts and ideas of Confucius, Mencius and XunKuang1. There have been lots of spirited and in-depth discussions on their ideological inheritance and development from all kinds of academics. This paper tries to cast a new light on these discussions through “machine reading2”.

An Unsupervised Method for Short-Text Sentiment Analysis Based on Analysis of Massive Data

Common forms of short text are microblogs, Twitter posts, short product reviews, short movie reviews and instant messages. Sentiment analysis of them has been a hot topic. A highly-accurate model is proposed in this paper for short-text sentiment analysis. The researches target microblog, product review and movie reviews. Words, symbols or sentences with emotional tendencies are proved important indicators in short-text sentiment analysis based on massive users’ data. It is an effective method to predict emotional tendencies of short text using these features. The model has noticed the phenomenon of polysemy in single-character emotional word in Chinese and discusses singlecharacter and multi-character emotional word separately. The idea of model can be used to deal with various kinds of short-text data. Experiments show that this model performs well in most cases.

Stress and Thermal Analysis of the In-Vessel Resonant Magnetic Perturbation Coils on the J-TEXT Tokamak

A set of four in-vessel saddle coils was designed to generate a helical field on the J- TEXT tokamak to study the influences of the external perturbation field on plasma. The coils are fed with alternating current up to 10 kA at frequency up to 10 kHz. Due to the special structure, complex thermal environment and limited space in the vacuum chamber, Jt is very important to make sure that the coils will not be damaged when undergoing the huge electromagnetic forces in the strong toroidal field, and that their temperatures don＇t rise too much and destroy the in- sulation. A 3D finite element model is developed in this paper using the ANSYS code, stresses are analyzed to find the worst condition, and a mounting method is then established. The results of the stress and modal analyses show that the mounting method meets the strength requirements. Finally, a thermal analysis is performed to study the cooling process and the temperature distribution of the coils.

Study on text analysis and advanced English teaching

This paper is attempted to explore advanced English teaching from perspective of text analysis. It involves the introduction of culture background, the application of genre-based approach, the appreciation of writing style and the analysis of textual structure through sample studies.

基于LDA-DLNB网络舆情识别的应急管理智库决策研究

[目的/意义]网络舆情信息在突发事件应对中有着不容忽视的传播影响力。完善舆论引导是中国特色新型智库的重要功能之一。做好网络舆情识别是应急管理智库提高综合研判能力的关键。[方法/过程]本文从应急管理智库建设角度出发,聚焦突发事件网络舆情信息中潜在的关键热点与情感结构识别问题,通过机器学习方法构建LDA-LDNB网络舆情信息挖掘模型,并基于突发事件网络舆情文本信息进行模型验证和实证分析。[结果/结论]本文构建的模型能很好地识别突发事件中网络舆情信息的主题、情感与演化特征,为应急管理智库提高应急管理决策服务能力提供方法支持,为构建数据驱动的应急管理智库提供新的思路。

“双减”政策何以有效落地?基于“任务-主体-结构”的政策文本分析和对S省的调查

聚焦“双减”政策何以有效落地问题进行了两个新尝试。一是在研究范式上,从应然逻辑、实然状态和必然进路三个层面回应。二是在分析框架上,融合政策执行过程相关研究和结构化理论构建了突出目标导向、强化结构分析的“任务-主体-结构”政策执行过程模型,更契合我国复杂的政策执行环境。基于对“双减”政策文本和S省的落地实践分析发现:第一阶段的落地过程呈现出“运动式”治理的特征,但实际执行结构、落地条件和落地成效在内涵上远未达到设定目标。建议着力推动向“动员式”转型,以高质量教育体系建设为总抓手,有效回应家长对政策的价值需求。

基于米特-霍恩模型的高校实验室安全管理制度执行困境及路径优化

为提高高校实验室安全管理规范相关文件的执行效果,以国家教育部、省教育厅、某高校发布的实验室安全管理相关制度为对象,引入米特-霍恩模型分析高校实验室安全管理制度的执行路径,并运用文本分析法寻找文件中与其作用因素相关的关键词,进行制度执行过程中的困境分析。研究结果表明,在实验室安全管理方面,高校可能会面临多重困境,包括实验室危险因素的多样化、实验室安全经费的不足、制度执行主体之间的交流分层化、职能重叠、制度执行者缺乏足够的实施驱动力等问题。文章对实验室安全管理制度执行进行了路径优化,旨在健全我国高校实验室安全监管机制,降低高校实验室事故发生率。

我国数据素养教育研究的发展趋势及优化策略探讨--基于VOSviewer工具文本分析视角

随着网络强国、数字中国战略的全面实施,数据素养教育引发了研究群体的广泛关注,成为深化数据融合应用,释放数据红利的重要手段。本文借助文本分析技术,研究分析中国知网中以“数据素养教育”为主题的1331篇文献,围绕发文量、发文机构、资助基金、关键词、研究主题等构建我国数据素养教育研究知识图谱,阐述我国数据素养教育研究领域的发展脉络及现状,基于资源依赖理论提出我国数据素养教育目前存在的问题,并给出优化对策。

The Probe of Significance of Text Analysis in Translation

This paper studies the significance of text analysis in translation in regard to the analysis both inside and outside the "text",discussing the weight of analyzing lexical units and stylistic scales in translation and examining the importance of analyzing the translator’s intention,the author’s intention and the target language(TL)readership.

江苏人才政策文本的多维分析--基于2013年至2022年政策文本

江苏人才政策的持续优化,有效促进了人才队伍建设,为推动江苏经济高质量发展提供了有力支撑。文章选取2013年1月1日至2022年12月31日的江苏省人民政府、江苏省科学技术厅以及各地级市政府所发布的196份江苏省内的人才政策文件为研究对象,构建了“政策工具—政策目标—政策主题”的三维分析框架,探究江苏人才政策文本的多维特征。研究发现,人才政策主要呈现出注重人力资源建设、培育人才创新能力、促进人才协调发展等特点。

区块链技术对企业全要素生产率的影响研究--基于信任构建和降本增效的视角

为了研究区块链技术对企业的生产效率的作用,文章以2019—2021年A股上市公司中非ST制造业企业为样本,运用文本分析及机器学习word2vec方法基于上市公司年报对企业区块链应用程度进行度量,实证检验了其与制造业企业全要素生产率之间的关系。研究发现上市公司区块链技术应用程度越高,其全要素生产率越高。机制研究发现,信任构建和降本增效,是区块链应用赋能全要素生产率提高的主要路径。进一步研究发现,企业子行业类型以及企业代理成本,会影响区块链应用对生产率的促进作用。

考虑舆情风险的商业银行风险评估研究--基于文本分析、因子分析和BP神经网络

近年来风险事件频发导致大众恐慌情绪上升,商业银行面临包括舆情风险在内的各种风险。构建考虑舆情的商业银行风险预警指标体系,以2012-2022年15家商业银行为研究样本,通过文本分析构建舆情指数,因子分析法构造风险指数。建立28×55×1的BP神经网络模型,通过弹性分析评估风险指数对每个评价指标的敏感性。研究发现,国有大型银行在风险评价中处于优势地位;BP神经网络可以很好地进行风险拟合;A41舆情指数引起风险指数变动的敏感性强。应加强舆情风险管理,建立多层次系统性商业银行风险预警指标体系,结合机器学习在内的多种分析方法加强风险评估水平。