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1.
Forecasting ; 4(4):767-786, 2022.
Article in English | Web of Science | ID: covidwho-2199954

ABSTRACT

Big data technology and predictive analytics exhibit advanced potential for business intelligence (BI), especially for decision-making. This study aimed to explore current research studies, historic developing trends, and the future direction. A bibliographic study based on CiteSpace is implemented in this paper, 681 non-duplicate publications are retrieved from databases of Web of Science Core Collection (WoSCC) and Scopus from 2000 to 2021. The countries, institutions, cited authors, cited journals, and cited references with the most academic contributions were identified. Social networks and collaborations between countries, institutions, and scholars are explored. The cross degree of disciplinaries is measured. The hotspot distribution and burst keyword historic trend are explored, where research methods, BI-based applications, and challenges are separately discussed. Reasons for hotspots bursting in 2021 are explored. Finally, the research direction is predicted, and the advice is delivered to future researchers. Findings show that big data and AI-based methods for BI are one of the most popular research topics in the next few years, especially when it applies to topics of COVID-19, healthcare, hospitality, and 5G. Thus, this study contributes reference value for future research, especially for direct selection and method application.

2.
Big Data and Cognitive Computing ; 6(4), 2022.
Article in English | Web of Science | ID: covidwho-2199724

ABSTRACT

Big Data has changed how enterprises and people manage knowledge and make decisions. However, when talking about Big Data, so many times there are different definitions about what it is and what it is used for, as there are many interpretations and disagreements. For these reasons, we have reviewed the literature to compile and provide a possible solution to the existing discrepancies between the terms Data Analysis, Data Mining, Knowledge Discovery in Databases, and Big Data. In addition, we have gathered the patterns used in Data Mining, the different phases of Knowledge Discovery in Databases, and some definitions of Big Data according to some important companies and organisations. Moreover, Big Data has challenges that sometimes are the same as its own characteristics. These characteristics are known as the Vs. Nonetheless, depending on the author, these Vs can be more or less, from 3 to 5, or even 7. Furthermore, the 4Vs or 5Vs are not the same every time. Therefore, in this survey, we reviewed the literature to explain how many Vs have been detected and explained according to different existing problems. In addition, we detected 7Vs, three of which had subtypes.

3.
Atmosphere ; 13(12):1984, 2022.
Article in English | Academic Search Complete | ID: covidwho-2199713

ABSTRACT

Vehicle mileage is one of the key parameters for accurately evaluating vehicle emissions and energy consumption. With the support of the national annual vehicle emission inspection networked platform in China, this study used big data methods to analyze the activity level characteristics of the light-duty passenger vehicle fleet with the highest ownership proportion. We found that the annual mileage of vehicles does not decay significantly with the increase in vehicle age, and the mileage of vehicles is relatively low in the first few years due to the run-in period, among other reasons. This study indicated that the average mileage of the private passenger car fleet is 10,300 km/yr and that of the taxi fleet was 80,000 km/yr in China in 2019, and the annual mileage dropped by 22% in 2020 due to the pandemic. Based on the vehicle mileage characteristics, the emission inventory of major pollutants from light-duty passenger vehicles in China for 2010–2020 was able to be updated, which will provide important data support for more accurate environmental and climate benefit assessments in the future. [ FROM AUTHOR]

5.
Frontiers in Public Health ; 10, 2022.
Article in English | Web of Science | ID: covidwho-2199499

ABSTRACT

AimTo explore the role of smell and taste changes in preventing and controlling the COVID-19 pandemic, we aimed to build a forecast model for trends in COVID-19 prediction based on Google Trends data for smell and taste loss. MethodsData on confirmed COVID-19 cases from 6 January 2020 to 26 December 2021 were collected from the World Health Organization (WHO) website. The keywords "loss of smell" and "loss of taste" were used to search the Google Trends platform. We constructed a transfer function model for multivariate time-series analysis and to forecast confirmed cases. ResultsFrom 6 January 2020 to 28 November 2021, a total of 99 weeks of data were analyzed. When the delay period was set from 1 to 3 weeks, the input sequence (Google Trends of loss of smell and taste data) and response sequence (number of new confirmed COVID-19 cases per week) were significantly correlated (P < 0.01). The transfer function model showed that worldwide and in India, the absolute error of the model in predicting the number of newly diagnosed COVID-19 cases in the following 3 weeks ranged from 0.08 to 3.10 (maximum value 100;the same below). In the United States, the absolute error of forecasts for the following 3 weeks ranged from 9.19 to 16.99, and the forecast effect was relatively accurate. For global data, the results showed that when the last point of the response sequence was at the midpoint of the uptrend or downtrend (25 July 2021;21 November 2021;23 May 2021;and 12 September 2021), the absolute error of the model forecast value for the following 4 weeks ranged from 0.15 to 5.77. When the last point of the response sequence was at the extreme point (2 May 2021;29 August 2021;20 June 2021;and 17 October 2021), the model could accurately forecast the trend in the number of confirmed cases after the extreme points. Our developed model could successfully predict the development trends of COVID-19. ConclusionGoogle Trends for loss of smell and taste could be used to accurately forecast the development trend of COVID-19 cases 1-3 weeks in advance.

6.
Statistical Journal of the IAOS ; : 1-7, 2022.
Article in English | Academic Search Complete | ID: covidwho-2198511

ABSTRACT

In 2020 and 2021, the challenges related to the decline in the financing of statistical production and the cooperation of respondents was exacerbated by the COVID-19 pandemic. This scenario led national statistical offices (NSOs) to accelerate consideration of alternative data sources to complement or even replace traditional survey data. In this context, the use of big data to produce statistics has become promising. The use of big data for statistics is already in practice in many parts of the Global North and has also been spreading rapidly in the South. Part of the success of this trend is due to the support of the United Nations Committee of Experts on Big Data and Data Science for Official Statistics (UNCEBD), in particular its four Regional Hubs for Big Data. To learn the extent of the use of big data for official statistics in Latin America and the Caribbean, the United Nations Regional Hub for Big Data in Brazil conducted a study of the practices of NSOs in the region. A very promising scenario was found regarding the use of big data from satellite imagery, web scraping and other big data sources, for applications such as the production of price statistics, land use and cover patterns and migration. [ FROM AUTHOR]

7.
JMIR public health and surveillance. ; 22, 2022.
Article in English | EMBASE | ID: covidwho-2198104

ABSTRACT

BACKGROUND: Highly effective COVID-19 vaccines are available and free of charge in the United States. With adequate coverage, their use may help return life back to normal and reduce COVID-related hospitalization and death. Many barriers to widespread inoculation have prevented herd immunity, including vaccine hesitancy, lack of vaccine knowledge, and misinformation. The Ad Council and COVID Collaborative have been conducting one of the largest nationwide targeted campaigns ("It's Up To You") to communicate vaccine information and encourage timely vaccination across the US. More than 300 major brands, digital and print media companies, and community-based organizations support the campaigns to reach distinct audiences. OBJECTIVE(S): The goal of this study was to utilize aggregated mobility data to assess the effectiveness of the campaign on COVID vaccine uptake. METHOD(S): Campaign exposure data were collected from the Cuebiq advertising impact measurement platform consisting of about 17 million opted-in and de-identified mobile devices across the country. A Bayesian spatio-temporal hierarchical model was developed to assess campaign effectiveness through estimating the association between county-level campaign exposure and vaccination rates reported by the Centers for Disease Control and Prevention. To minimize potential bias in exposure to the campaign, the model included several control variables (age, race/ethnicity, income, and political affiliation). We also incorporated Conditionally Autoregressive (CAR) residual models to account for apparent spatio-temporal autocorrelation. RESULT(S): The dataset covers a panel of 3,104 counties from 48 states and the District of Columbia during a period of 22 weeks (March 29 - August 29, 2021). Officially launched in February 2021, the campaign reached about 3% of the anonymous devices on the Cuebiq platform by the end of March, which was the start of the study period. That exposure rate gradually declined to slightly above 1% in August 2021, effectively ending the study period. Results from the Bayesian hierarchical model indicate a statistically significant positive association between campaign exposure and vaccine uptake at the county level. A campaign that reaches everyone would boost the vaccination rate by 2.2% (95% uncertainty interval: 2.0 - 2.4%) on a weekly basis, compared to the baseline case of no campaign. CONCLUSION(S): The "It's Up To You" campaign is effective in promoting COVID vaccine uptake, suggesting that a nationwide targeted mass media campaign with multi-sectoral collaborations could be an impactful health communication strategy to improve progress against this and future pandemics. Methodologically, the results also show that location intelligence and mobile-phone based monitoring platforms can be effective in measuring impact of large-scale digital campaigns in near-real-time. CLINICALTRIAL: Not Applicable.

8.
International Journal of Public Policy ; 16(5-6):362-378, 2022.
Article in English | Scopus | ID: covidwho-2197263

ABSTRACT

This study aims to introduce the potential of volatility, uncertainty, complexity, and ambiguity (VUCA) as a framework to explain the characteristics of current wicked policy risks. We also analyse how big data informatics help us to overcome volatile, uncertain, complex, and ambiguous wicked policy risks in the context of the COVID-19 pandemic. Specifically, presenting a VUCA framework, this study illustrates how policy risks disturb policy results based on selected cases. Additionally, we argue that big data analytics has become an increasingly significant and feasible instrument for managing policy risks. We discuss potential challenges concerning skepticism about continued politics in policy decisions and implementation processes, and limitations of informatics and the nature of big data, which is often possibly biased and incomplete. Copyright © 2022 Inderscience Enterprises Ltd.

9.
BMC Health Services Research ; 22:1-11, 2022.
Article in English | ProQuest Central | ID: covidwho-2196253

ABSTRACT

Objective The Department of Veterans Affairs' (VA) electronic health records (EHR) offer a rich source of big data to study medical and health care questions, but patient eligibility and preferences may limit generalizability of findings. We therefore examined the representativeness of VA veterans by comparing veterans using VA healthcare services to those who do not. Methods We analyzed data on 3051 veteran participants age ≥ 18 years in the 2019 National Health Interview Survey. Weighted logistic regression was used to model participant characteristics, health conditions, pain, and self-reported health by past year VA healthcare use and generate predicted marginal prevalences, which were used to calculate Cohen's d of group differences in absolute risk by past-year VA healthcare use. Results Among veterans, 30.4% had past-year VA healthcare use. Veterans with lower income and members of racial/ethnic minority groups were more likely to report past-year VA healthcare use. Health conditions overrepresented in past-year VA healthcare users included chronic medical conditions (80.6% vs. 69.4%, d = 0.36), pain (78.9% vs. 65.9%;d = 0.35), mental distress (11.6% vs. 5.9%;d = 0.47), anxiety (10.8% vs. 4.1%;d = 0.67), and fair/poor self-reported health (27.9% vs. 18.0%;d = 0.40). Conclusions Heterogeneity in veteran sociodemographic and health characteristics was observed by past-year VA healthcare use. Researchers working with VA EHR data should consider how the patient selection process may relate to the exposures and outcomes under study. Statistical reweighting may be needed to generalize risk estimates from the VA EHR data to the overall veteran population.

10.
Bmc Medical Ethics ; 23(1), 2022.
Article in English | Web of Science | ID: covidwho-2196237

ABSTRACT

The aim of UK-REACH ( "The United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers ") is to understand if, how, and why healthcare workers (HCWs) in the United Kingdom (UK) from ethnic minority groups are at increased risk of poor outcomes from COVID-19. In this article, we present findings from the ethical and legal stream of the study, which undertook qualitative research seeking to understand and address legal, ethical, and social acceptability issues around data protection, privacy, and information governance associated with the linkage of HCWs' registration data and healthcare data. We interviewed 22 key opinion leaders in healthcare and health research from across the UK in two-to-one semi-structured interviews. Transcripts were coded using qualitative thematic analysis. Participants told us that a significant aspect of Big Data research in public health is varying drivers of mistrust-of the research itself, research staff and funders, and broader concerns of mistrust within participant communities, particularly in the context of COVID-19 and those situated in more marginalised community settings. However, despite the challenges, participants also identified ways in which legally compliant and ethically informed approaches to research can be crafted to mitigate or overcome mistrust and establish greater confidence in Big Data public health research. Overall, our research indicates that a "Big Data Ethics by Design " approach to research in this area can help assure (1) that meaningful community and participant engagement is taking place and that extant challenges are addressed, and (2) that any new challenges or hitherto unknown unknowns can be rapidly and properly considered to ensure potential (but material) harms are identified and minimised where necessary. Our findings indicate such an approach, in turn, will help drive better scientific breakthroughs that translate into medical innovations and effective public health interventions, which benefit the publics studied, including those who are often marginalised in research.

11.
Environment and Planning B-Urban Analytics and City Science ; 2022.
Article in English | Web of Science | ID: covidwho-2195941

ABSTRACT

As the mobile Internet emerges, numerous Instagram-worthy locations gradually constitute new spaces of urban tourism. For instance, the Xiaohongshu application, a community with shared content, has increasingly become a platform for people to share well-known tourist attractions, providing a new perspective for the study of the popularity of tourism spaces. On the basis of data of ticking off Instagram-worthy locations from the Xiaohongshu application, the present study aims to identify tourism hotspots in Beijing, analyze their spatial characteristics, and explore their evolution features from two dimensions of time and space. In addition, the emotional images of tourism hotspots in Beijing are interpreted by semantic analysis with an internal mechanism that influences those locations explored. The results of the study show that (1) the overall spatial structure of tourism hotspots in Beijing is C-shaped, which expands from the core area to the periphery with the feature of a circle layer. (2) under the influence of the COVID-19 pandemic, the spatial distribution center of tourism hotspots in Beijing is gradually shifting to the Southeast with the tendency of expanding to the surrounding suburbs. (3) the reception and serviceability of the tourist attractions have a significant influence on the popularity of tourism hotspots. To date, less research has been focused on the data of ticking off emerging Instagram-worthy locations like the Xiaohongshu application, and there is a dearth of the study related to in-depth excavation of the internal influencing mechanism of their popularity. This paper, therefore, under the interaction of virtual and reality, provides new ideas and methods for studying the popularity of urban tourist attractions.

12.
5th International Conference on Computer Science and Software Engineering, CSSE 2022 ; : 707-712, 2022.
Article in English | Scopus | ID: covidwho-2194140

ABSTRACT

Falls, considered a serious health-related concern for the elderly people, are associated with multiple diverse and dynamic needs for the elderly people themselves, their caregivers, their family members, and healthcare professionals. The modern-day Internet of Everything lifestyle is characterized by people using the internet for a multitude of reasons which also includes seeking and sharing information related to such needs. Such activity on the internet results in the generation of tremendous amounts of web behavior-based Big Data which can be studied and analyzed to investigate the trends in the underlining needs and the associated web search interests. The COVID-19 pandemic that the world is facing right now has impacted the elderly population to a significant extent. In fact, the elderly population is considered a demographic group that is most likely to get infected by this virus and develop serious symptoms, which could lead to hospitalizations and death. There hasn't been any study conducted in the field of aging research thus far that investigates how the COVID-19 pandemic may or may not have impacted the needs related to fall detection in the elderly. This work aims to address this research challenge. A dedicated methodology based on Google Trends is proposed in this paper that studies the web behavior-based Big Data related to fall detection from different countries both before and after the pandemic. The preliminary results presented from the analysis of the web behavior-based Big Data from 14 countries - USA, India, Germany, United Kingdom, Spain, Australia, Indonesia, Malaysia, Thailand, South Africa, Canada, Philippines, Sweden, and Ireland, which are amongst the countries worst hit by COVID-19, shows evidence that the pandemic had an impact towards increasing the web search interests related to fall detection in multiple countries. © 2022 ACM.

13.
5th International Conference on Information Management and Management Science, IMMS 2022 ; : 39-45, 2022.
Article in English | Scopus | ID: covidwho-2194116

ABSTRACT

Global public emergencies represented by COVID-19 have posed new requirements and challenges to the current governance systems and capacities of governments. Mining decision-level intelligence from massive, multi-source and heterogeneous big data is the basic environment of current emergency decision making. This paper is based on the case analysis of COVID-19 epidemic prevention and control and constructs the intelligent decision-making process framework of emergency intelligence from the basic support layer, information and data layer, fusion output layer, emergency decision-making layer and main user layer. A risk governance system of "government-led, enterprise participation, media coordination, social crowdsourcing"has been established with big data co-construction and sharing and joint epidemic prevention and control. Through intelligence collection, processing, analysis and output transmission of epidemic-related data and information in the big data environment, it provides services for the emergency prevention and control of major epidemics in both normal and abnormal situations. In the front-end control phase, the emergency intelligence perception of the source quality is ensured to support the emergency decision, and the wisdom level of emergency decision-making is improved by optimizing the use of terminal intelligence and failure prevention methods. This paper proposes to optimize the intelligent decision-making process of emergencies from the effective prevention strategy of intelligence perception failure and the effective intervention strategy of decision failure prevention. This article explores the big data in the application of global public emergency management and innovation, change and revelation, to expand the emergency decision-making from the perspective of intelligence applications, provides with wisdom for the government emergency decision. © 2022 ACM.

14.
6th International Conference on Big Data Research, ICBDR 2022 ; : 48-54, 2022.
Article in English | Scopus | ID: covidwho-2194114

ABSTRACT

Business performance has increased dramatically owing to the increase use of multichannel services in global markets posed by COVID-19 pandemic new normal. The viability of commercial airports depends on strong business models that integrates multichannel services such as digital services, cloud services and big data analytics services to its building blocks. This paper examines how multichannel services and big data analytics services can be used to optimize and enhance airport business building blocks to its business models to increase airports' revenue and business value. A case study was conducted to analyze PNG's airport authority's (National Airports Corporation (NAC) existing business models in comparison to the Osterwalder's business model building blocks. A sustainable business model was proposed that integrates digital and web-based technologies to boost the model's commercial and operational viability. © 2022 ACM.

15.
Acm Transactions on Spatial Algorithms and Systems ; 8(4), 2022.
Article in English | Web of Science | ID: covidwho-2194077

ABSTRACT

High resolution mobility datasets have become increasingly available in the past few years and have enabled detailed models for infectious disease spread including those for COVID-19. However, there are open questions on how such mobility data can be used effectively within epidemic models and for which tasks they are best suited. In this paper, we extract a number of graph-based proximity metrics from high resolution cellphone trace data from X-Mode and use it to study COVID-19 epidemic spread in 50 land grant university counties in the US. We present an approach to estimate the effect of mobility on cases by fitting an ordinary differential equation based model and performing multivariate linear regression to explain the estimated time varying transmissibility. We find that, while mobility plays a significant role, the contribution is heterogeneous across the counties, as exemplified by a subsequent correlation analysis. We also evaluate the metrics' utility for case surge prediction defined as a supervised classification problem, and show that the learnt model can predict surges with 95% accuracy and an 87% F1-score.

16.
Journal of Industrial Integration and Management-Innovation and Entrepreneurship ; 07(04):515-533, 2022.
Article in English | Web of Science | ID: covidwho-2194055

ABSTRACT

Internet of Things (IoT) is a critical component of Industry 4.0. It has extensive applications in the monitoring of production systems in manufacturing and services. This technology opens up newer and innovative possibilities in manufacturing by facilitating higher performance. IoT's major capability is to collect and share information with the help of internet-connected machines and devices. It is associated with unique identification numbers or codes that can be controllable through our daily use devices like smartphones. This technology's major components are software, hardware with the network's connectivity for data altercation, and collection. IoT creates disruptive innovation in the field of manufacturing. The need is to understand this technology and how it can help the contemporary production systems. Here, we have studied the potential of this technology to provide better solutions in Industry 4.0. The major drivers of IoT for Industry 4.0 are studied. This paper discusses how Industry 4.0 helps create a smart factory. Finally, we have identified and studied significant IoT applications to adopt Industry 4.0 successfully, and the same is presented in tabular form. With proper implementation of this technology, industries observe an improvement in efficiency during the manufacturing of products. Manufacturing is done with lesser cost and errors. However, there is a long way to reap full benefits for humankind.

17.
The Competitiveness of Nations 1: Navigating the US-China Trade War and the COVID-19 Global Pandemic ; : 183-210, 2022.
Article in English | Scopus | ID: covidwho-2194021

ABSTRACT

The COVID-19 pandemic has presented a serious threat to mental health on a global scale. With unemployment worldwide reaching staggering highs and the loss of job security for millions of people, recent studies suggest that the pandemic can be linked to elevated anxiety levels, psychological distress, depression, PTSD, and suicidal behavior. While the case number of those infected by the COVID-19 virus (more than 170 million infected and more than 3.5 million deaths on May 30, 2021) certainly poses an unprecedented global health challenge, the detrimental effects on the population's mental health are far more difficult to measure, and thus address. Governments around the world are therefore tasked with providing more effective and affordable solutions to tackling both aspects of the pandemic's impact via increased investment in healthcare, medication, and vaccine distribution, as well as through an emphasis on innovative approaches to improve mental healthcare. Digital innovation and AI have already shown their potential for both prevention and treatment of mental health problems, as valid supplements for mental health practitioners.Effective combatting of the pandemic's consequences on mental health is crucial for rebuilding sustainable and competitive economies in the post-COVID-19 recovery. © 2022 by World Scientific Publishing Co. Pte. Ltd.

18.
Ieee Transactions on Engineering Management ; 2022.
Article in English | Web of Science | ID: covidwho-2192084

ABSTRACT

The unprecedented development of digital technologies and the COVID-19 pandemic environment have accelerated digital transformation in all industries, forcing companies of all types and sizes to change the way they operate. In such an environment, companies need to quickly adapt their business models and demonstrate ability to implement strategic changes that would result in creating and delivering new value with digital business models (DBMs). Innovation and continuous improvement of a DBM lead to its higher maturity and resilience to unexpected changes. Given that the development and implementation of a DBM is often very unpredictable and the relevant literature is still scarce, many questions on how to develop an effective and sufficiently mature DBM remain unanswered. To contribute to finding the relevant answers, this article explores which aspects are important for the maturity of a DBM and how DBM maturity can be measured, particularly for small and medium enterprises (SMEs). In order to address such a research question, each of the three components of a DBM (content, experience, and platform) was empirically tested through a structured survey questionnaire on a sample of 162 SME companies from 42 countries in 5 continents. The article contributes to research literature by proposing and empirically validating a framework for measuring DBM maturity, particularly for SMEs. The measurement framework has been found to be consistent and reliable. Furthermore, the article found that user-generated content and user experience tracking are very important aspects to address for reaching and sustaining DBM maturity, but a high proportion of surveyed companies did not pay due attention to their implementation. This finding contributes to identifying tangible improvement areas for the comparable type of companies.

19.
14th International Conference on Information Technology and Electrical Engineering, ICITEE 2022 ; : 136-141, 2022.
Article in English | Scopus | ID: covidwho-2191882

ABSTRACT

Mobile Positioning Data (MPD) contains information on the location of the mobile phone by approximating mobile phones' location relative to fixed infrastructures (e.g., telecommunication towers that transmit signals). While the data query is technically straightforward, obtaining this dataset requires particular permission to protect customers' privacy. Additionally, the dataset has large volumes of data (i.e, up to 300GB per day), resulting in not many researchers holding this data source to analyze the mobility of people. In this work, we collaborate with one of the biggest telecommunication service providers in Indonesia to collect MPD and prepare the big data infrastructure. We thus analyze mobility patterns during the early phase of COVID-19 in 2020 using actual Mobile Positioning Data in five provinces in Java. We use three metrics, namely, the number of visits, averaged travel distance, and Origin-Destination matrix. The findings indicate that the social restriction in the corresponding provinces has reduced the average traveled distance of the people, but not their number of visits. That is, while the traveled distance has declined more than eight times compared to the baseline, the number of visits may rocket up, up to nine times. It indicates that people are still having shorter trips even though their regular activities (working, schooling, etc.) have been restricted. The data also show that during Ramadhan month, the government has a successful intervention in restricting people for mudik Lebaran, The number of visits dropped to below 30 visits during Ramadhan and only small spikes exist during 'libur lebaran'. © 2022 IEEE.

20.
2022 International Conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2022 ; : 122-126, 2022.
Article in English | Scopus | ID: covidwho-2191838

ABSTRACT

Economic and fiscal policies devised by international organizations, governments, and central banks rely significantly on economic projections, particularly during times of economic instability such as the one we have recently seen with the COVID-19 virus spreading globally. However, the accuracy of economic forecasting and now casting models remains a challenge since modern economies are prone to multiple shocks that make forecasting and now casting activities extremely difficult, both in the short and medium range. The purpose of the paper is to identify the key aspects, which must be taken into account in big data sentiment analysis to solve the problem of forecasting and now casting tasks. The work has developed an mpBC-ELMo based BNM-cBLSTM for financial sentiment analysis in European Bond markets. The proposed framework is processed based on collection of big data that are formed into a corpus. Initially, the corpus data is subjected to preprocessing, which performs tokenization, Lemmatization, URL removal, punctuations removal, Dependency Parsing, Noun Phrases, Named Entity recognition, and Coreference Resolution in order to make the data healthier and to get a potential impact for analyzing the sentiments. Thereafter, the data is converted into a vector using mpBC-ELMo, which addresses the complex characteristics of the word as well as polarity and handles stock bond correlation invariant, behavioral biases. Finally, BNM-cBLSTM analyses the sentiments and provides an accurate as well as optimized improvisation. In comparison to existing state-of-the-art methods, experimental results show that the work tends to deliver a precise sentiment analysis and avoids erroneous prediction rate. © 2022 IEEE.

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