检索结果-南通市图书馆

COVID-19 Pandemic Data Predict the Stock Market

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2021年第3期36卷 451-460页

作者： Abdulaziz Almehmadi Faculty of Computing and Information Technology University of TabukTabukSaudi Arabia

Unlike the 2007–2008 market crash,which was caused by a banking failure and led to an economic recession,the 1918 inﬂuenza pandemic triggered a worldwideﬁnancial *** usually affect the global economy,and the COVID-19 pandemic is no *** stock markets have fallen over 40%,and companies are shutting down,ending contracts,and issuing volun-tary and involuntary leaves for thousands of *** economic effects have led to an increase in unemployment rates,crime,and *** pandemics’economic effects,especially on the stock market,has not been urgent or feasible until ***,with advances in artiﬁcial intelligence(AI)and the inter-connectivity that social media provides,such research has become *** this paper,we propose a COVID-19-based stock market prediction system(C19-SM2)that utilizes social *** AI system enables economists to study how COVID-19 pandemic data inﬂuence social media and,hence,the stock market.C19-SM2 gathers COVID-19 infection and death cases reported by the authorities and social media data from a geographic area and extracts the sentiments and events that occur in that *** information is then fed to the support vector machine(SVM)and random forest and random tree classiﬁers along with current stock market ***,the system produces a projection of the stock market’s movement during the next *** tested the system with the Dow Jones Industrial Average(DJI)and the Tadawul All Share Index(TASI).Our system achieved a stock market prediction accuracy of 99.71%,substantially higher than the 89.93%accuracy reported in the related literature;the inclusion of COVID-19 data improved accuracy by 9.78%.

关键词： COVID-19 economic impact artiﬁcial intelligence sentiment analysis stock market

COVID-19 Public sentiment Insights: A Text Mining Approach to the Gulf Countries

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第5期67卷 1613-1627页

作者： Saleh Albahli Ahmad Algsham Shamsulhaq Aeraj Muath Alsaeed Muath Alrashed Hafiz Tayyab Rauf Muhammad Arif Mazin Abed Mohammed Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science Univesity of GurjatPakistan School of Computer Science Guanzghou UniversityGuangzhou510006China College of Computer Science and Information technology University of Anbar11RamadiAnbarIraq

Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their *** COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media *** phenomenon caused a state of panic among *** studies were conducted to stop the spread of fake news to help people cope with the *** this paper,a semantic analysis of three levels(negative,neutral,and positive)is used to gauge the feelings of Gulf countries towards the pandemic and the lockdown,on basis of a Twitter dataset of 2 months,using Natural Language Processing(NLP)*** has been observed that there are no mixed emotions during the pandemic as it started with a neutral reaction,then positive sentiments,and lastly,peaks of negative *** results show that the feelings of the Gulf countries towards the pandemic depict approximately a 50.5%neutral,a 31.2%positive,and an 18.3%negative sentiment *** study can be useful for government authorities to learn the discrepancies between different populations from diverse areas to overcome the COVID-19 spread accordingly.

关键词： COVID-19 sentiment analysis natural language processing twitter social data mining sentiment polarity

Feature-aware regularization for sparse online learning

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2014年第5期57卷 65-85页

作者： OIWA Hidekazu MATSUSHIMA Shin NAKAGAWA Hiroshi Graduate School of Information Science and Technology The University of Tokyo Information Technology Center The University of Tokyo

Learning a compact predictive model in an online setting has recently gained a great deal of *** combination of online learning with sparsity-inducing regularization enables faster learning with a smaller memory space than the previous learning *** optimization methods and learning algorithms have been developed on the basis of online learning with L1-regularization.L1-regularization tends to truncate some types of parameters,such as those that rarely occur or have a small range of values,unless they are emphasized in ***,the inclusion of a pre-processing step would make it very difficult to preserve the advantages of online *** propose a new regularization framework for sparse online *** focus on regularization terms,and we enhance the state-of-the-art regularization approach by integrating information on all previous subgradients of the loss function into a regularization *** resulting algorithms enable online learning to adjust the intensity of each feature’s truncations without pre-processing and eventually eliminate the bias of *** show theoretical properties of our framework,the computational complexity and upper bound of *** demonstrated that our algorithms outperformed previous methods in many classification tasks.

关键词： online learning supervised learning sparsity-inducing regularization feature selection sentiment analysis

Ensemble Based Temporal Weighting and Pareto Ranking (ETP) Model for Effective Root Cause analysis

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第10期69卷 819-830页

作者： Naveen Kumar Seerangan S.Vijayaragavan Shanmugam Department of Computer Science(Category-B) Bharathiar UniversityCoimbatore641046Tamil NaduIndia CSE Muthayammal Engineering CollegeRasipuram637408Tamil NaduIndia

Root-cause identification plays a vital role in business decision making by providing effective future directions for the *** extraction and sentiment extraction plays a vital role in identifying the *** paper proposes the Ensemble based temporal weighting and pareto ranking(ETP)model for Root-cause *** extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble *** obtained aspects are validated and ranked using the proposed aspect weighing ***-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision *** were performed with the standard five product benchmark *** on all five product reviews indicate the effective performance of the proposed *** are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection *** results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%–15%and detection rates at 4%–24%compared to the state-of-the-art models.

关键词： Root cause analysis sentiment analysis aspect extraction ensemble modelling temporal weighting pareto ranking

Computing the User Experience via Big Data analysis:A Case of Uber Services

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第6期67卷 2819-2829页

作者： Jang Hyun Kim Dongyan Nan Yerin Kim Hyung Park Min Department of Interaction Science/Department of Human-Artificial Intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea Department of Applied Artificial Intelligence/Department of Human-Artificial Intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea

As of 2020,the issue of user satisfaction has generated a significant amount of ***,we employ a big data approach for exploring user satisfaction among Uber *** develop a research model of user satisfaction by expanding the list of user experience(UX)elements(i.e.,pragmatic,expectation confirmation,hedonic,and burden)by including more elements,namely:risk,cost,promotion,anxiety,sadness,and ***,we collect 125,768 comments from online reviews of Uber services and perform a sentiment analysis to extract the UX *** results of a regression analysis reveal the following:hedonic,promotion,and pragmatic significantly and positively affect user satisfaction,while burden,cost,and risk have a substantial negative ***,the influence of expectation confirmation on user satisfaction is not ***,sadness,anxiety,and anger are positively related to the perceived risk of *** with sadness and anxiety,anger has a more important role in increasing the perceived burden of *** on these findings,we also provide some theoretical implications for future UX literature and some core suggestions related to establishing strategies for Uber and similar *** proposed big data approach may be utilized in other UX studies in the future.

关键词： User satisfaction user experience big data sentiment analysis Uber

Modeling Multi-Targets sentiment Classification via Graph Convolutional Networks and Auxiliary Relation

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2020年第8期64卷 909-923页

作者： Ao Feng Zhengjie Gao Xinyu Song Ke Ke Tianhao Xu Xuelei Zhang Department of Computer Science Chengdu University of Information TechnologyChengdu610225China Central Washington University Des MoinesWA 98198USA

Existing solutions do not work well when multi-targets coexist in a *** reason is that the existing solution is usually to separate multiple targets and process them *** the original sentence has N target,the original sentence will be repeated for N times,and only one target will be processed each *** some extent,this approach degenerates the fine-grained sentiment classification task into the sentence-level sentiment classification task,and the research method of processing the target separately ignores the internal relation and interaction between the *** on the above considerations,we proposes to use Graph Convolutional Network(GCN)to model and process multi-targets appearing in sentences at the same time based on the positional relationship,and then to construct a graph of the sentiment relationship between targets based on the difference of the sentiment polarity between target *** addition to the standard target-dependent sentiment classification task,an auxiliary node relation classification task is *** demonstrate that our model achieves new comparable performance on the benchmark datasets:SemEval-2014 Task 4,i.e.,reviews for restaurants and ***,the method of dividing the target words into isolated individuals has disadvantages,and the multi-task learning model is beneficial to enhance the feature extraction ability and expression ability of the model.

关键词： Deep learning sentiment analysis graph convolutional networks(GCN)

Predicting Stock Using Microblog Moods

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

China Communications 2016年第8期13卷 244-257页

作者： Danfeng Yan Guang Zhou Xuan Zhao Yuan Tian Fangchun Yang State Key Laboratory of Networking & Switching Technology Beijing University of Posts and Telecommunications

Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS(Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.

关键词： stock prediction microblog sentiment analysis

维普期刊数据库

A Semi-Supervised Topic Model Incorporating sentiment and Dynamic Characteristic

在线全文

学校读者我要写书评

暂无评论

China Communications 2016年第12期13卷 162-175页

作者： Lanshan Zhang Xi Ding Ye Tian Xiangyang Gong Wendong Wang Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and Telecommunications State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications

With the rapid popularization of social applications, various kinds of social media have developed into an important platform for publishing information and expressing opinion. Detecting hidden topics from the huge amount of user-generated contents is of great commerce value and social significance. However traditional text analysis approachesonly focus on the statistical correlation between words, but ignore the sentiment tendency and the temporal properties which may have great effects on topic detection results. This paper proposed a Dynamic sentiment-Topic(DST) model which can not only detect and track the dynamic topics but also analyze the shift of public's sentiment tendency towards certain ***-Maximization algorithm was used in DST model to estimate the latent distribution, and we used Gibbs sampling method to sample new document set and update the hyper parameters and *** are conducted on a real dataset and the results show that DST model outperforms the existing algorithms in terms of topic detection and sentiment accuracy.

关键词： dynamic sentiment-topic model sentiment analysis topic detection

维普期刊数据库

Portraying User Life Status from Microblogging Posts

在线全文

学校读者我要写书评

暂无评论

Tsinghua Science and Technology 2013年第2期18卷 182-195页

作者： Jiayu Tang Zhiyuan Liu Maosong Sun Jiahua Liu the State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University

Microblogging services nformation and express opinions pro by vide a novel and popular communication scheme for Web users to share publishing short posts, which usually reflect the users＇ daily life. We can thus model the users＇ daily status and interests according to their posts. Because of the high complexity and the large amount of the content of the microblog users＇ posts, it is necessary to provide a quick summary of the users＇ life status, both for personal users and commercial services. It is non-trivial to summarize the life status of microblog users, particularly when the summary is conducted over a long period. In this paper, we present a compact interactive visualization prototype, LifeCircle, as an efficient summary for exploring the long-term life status of microblog users. The radial visualization provides multiple views for a given microblog user, including annual topics, monthly keywords, monthly sentiments, and temporal trends of posts. We tightly integrate interactive visualization with novel and state-of-the-art microblogging analytics to maximize their advantages. We implement LifeCircle on Sina Weibo, the most popular microblogging service in China, and illustrate the effectiveness of our prototype with various case studies. Results show that our prototype makes users nostalgic and makes them reminiscent about past events, which helps them to better understand themselves and others

关键词： text visualization microblogging topic model sentiment analysis keyword extraction

维普期刊数据库