An improved BERT method for the evolution of network public opinion of major infectious diseases: Case Study of COVID-19

Meng, Sheng; Cheng, Dapeng; Xu, Yan; Weng, Futian

doi:10.1016/j.eswa.2023.120938

Cited by 8 publications

(4 citation statements)

References 50 publications

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“…Machine learning methods are widely applied in many fields (Liu et al, 2023; Su et al, 2023; Weng et al, 2021), especially the energy consumption literature, as they are capable to deal with critical problems, such as data missing and the optimization of hyperparameters. For example, Ulucak (2021) used bootstrap auto‐regressive distributive lag to verify the relationship between financial development and energy consumption.…”

Section: Empirical Models and Datamentioning

confidence: 99%

The effects of governance quality on renewable and nonrenewable energy consumption: An explainable decision frame

Weng,

Cheng,

Zhuang

et al. 2024

Journal of Forecasting

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This study analyzes the effect of governance quality (six aspects: government effectiveness; control of corruption; voice and accountability; regulatory quality; political stability and absence of violence; and rule of law) on the renewable and nonrenewable energy consumption prediction based on the SHapely Additive exPlanations method for model analysis and interpretability. The empirical findings indicate that the time‐varying contributions of six aspects of governance quality on nonrenewable (renewable) energy consumption predicting vary greatly in E‐7 and G‐7 countries. The time‐varying contribution of governance quality within countries is heterogeneous and asymmetrical, especially India (Germany) in E‐7 countries (G‐7 countries). The prediction contribution distribution of governance quality between countries is more discrete in G‐7 countries than E‐7 countries. Our results are of great importance to policymakers and investors for enhancing the renewable energy consumption level in overcoming environmental challenges based on the country itself through governance quality.

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Section: Empirical Models and Datamentioning

confidence: 99%

The effects of governance quality on renewable and nonrenewable energy consumption: An explainable decision frame

Weng,

Cheng,

Zhuang

et al. 2024

Journal of Forecasting

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“…In Equation (10), f t determines which part of the history information is eliminated, producing values in the range 0 to 1. z t determines which data should be fed into the network, making output values in the range of 0 to 1. l t determines which network outputs are utilized as the final output and which contents of the current cell should be transmitted to the hidden layer h t , with output values from 0 to 1. ŷt is an expression for the predicted output.…”

Section: Quantile Regression Bi-directional Long Short-term Memory (Q...mentioning

confidence: 99%

“…For instance, during the Global Financial Crisis of 2008, gold prices saw a notable rise as investors sought refuge from the volatility of traditional stock markets. Similarly, the prolonged duration of the COVID-19 pandemic has saturated the investment and business areas [9][10][11]. Owing to its substantial effects, governments globally have implemented a variety of urgent actions at the beginning of the COVID-19 outbreak [12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Deterministic Probabilistic Forecasting Framework for Gold Price with a New Pandemic Index Based on Quantile Regression Deep Learning and Multi-Objective Optimization

Wang,

Lin

2023

Mathematics

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The significance of precise gold price forecasting is accentuated by its financial attributes, mirroring global economic conditions, market uncertainties, and investor risk aversion. However, predicting the gold price is challenging due to its inherent volatility, influenced by multiple factors, such as COVID-19, financial crises, geopolitical issues, and fluctuations in other metals and energy prices. These complexities often lead to non-stationary time series, rendering traditional time series modeling methods inadequate. Our paper presents a multi-objective optimization algorithm that refines the interval prediction framework with quantile regression deep learning in response to this issue. This framework comprehensively responds to gold’s financial market dynamics and uncertainties with a screening process of various factors, including pandemic-related indices, geopolitical indices, the US dollar index, and prices of various commodities. The quantile regression deep-learning models optimized by multi-objective optimization algorithms deliver robust, interpretable, and highly accurate predictions for handling non-linear relationships and complex data structures and enhance the overall predictive performance. The results demonstrate that the QRBiLSTM model, optimized using the MOALO algorithm, delivers excellent forecasting performance. The composite indicator AIS reaches −15.6240 and −11.5581 at 90% and 95% confidence levels, respectively. This underscores the model’s high forecasting accuracy and its potential to provide valuable insights for assessing future trends in gold prices. The deterministic and probabilistic forecasting framework for gold prices captures the market dynamics with the new pandemic index and comprehensively sets a new benchmark for predictive modeling in volatile market commodities like gold.

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“…User portraits are created by leveraging extensive user behavior data to generate a comprehensive and detailed description of their characteristics, transforming the data into a valuable tool for problem-solving purposes [20]. The wide application of user portraits has also made positive contributions in intrusion detection [21], personalized recommendation [22], medical [18], public opinion analysis [23], and other fields. Conventional mainstream user profiling methods encompass collaborative filtering, content-based, and knowledge-based approaches [24].…”

Section: Introductionmentioning

confidence: 99%

Consumer Default Risk Portrait: An Intelligent Management Framework of Online Consumer Credit Default Risk

Zhu,

Shia,

et al. 2024

Mathematics

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Online consumer credit services play a vital role in the contemporary consumer market. To foster their sustainable development, it is essential to establish and strengthen the relevant risk management mechanism. This study proposes an intelligent management framework called the consumer default risk portrait (CDRP) to mitigate the default risks associated with online consumer loans. The CDRP framework combines traditional credit information and Internet platform data to depict the portrait of consumer default risks. It consists of four modules: addressing data imbalances, establishing relationships between user characteristics and the default risk, analyzing the influence of different variables on default, and ultimately presenting personalized consumer profiles. Empirical findings reveal that “Repayment Periods”, “Loan Amount”, and “Debt to Income Type” emerge as the three variables with the most significant impact on default. “Re-payment Periods” and “Debt to Income Type” demonstrate a positive correlation with default probability, while a lower “Loan Amount” corresponds to a higher likelihood of default. Additionally, our verification highlights that the significance of variables varies across different samples, thereby presenting a personalized portrait from a single sample. In conclusion, the proposed framework provides valuable suggestions and insights for financial institutions and Internet platform managers to improve the market environment of online consumer credit services.

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An improved BERT method for the evolution of network public opinion of major infectious diseases: Case Study of COVID-19

Cited by 8 publications

References 50 publications

The effects of governance quality on renewable and nonrenewable energy consumption: An explainable decision frame

The effects of governance quality on renewable and nonrenewable energy consumption: An explainable decision frame

A Novel Deterministic Probabilistic Forecasting Framework for Gold Price with a New Pandemic Index Based on Quantile Regression Deep Learning and Multi-Objective Optimization

Consumer Default Risk Portrait: An Intelligent Management Framework of Online Consumer Credit Default Risk

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