An ontology-based Web mining method for unemployment rate prediction

Li, Ziang; Xu, Wei; Zhang, Likuan; Lau, Raymond Y. K.

doi:10.1016/j.dss.2014.06.007

Cited by 43 publications

(15 citation statements)

References 31 publications

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“…e concept of ontology came from the field of philosophy; ontology is often represented by a hierarchy of concepts and some other semantic information. According to the related work, domain ontology focuses on the concepts and their relationships to a particular domain [33]. Besides, domain ontology represents the axioms (e.g., rules) and constraints that define the foremost features of the domain [34][35][36].…”

Section: Traditional Feature Extractionmentioning

confidence: 99%

The Effects of Online Information on E-Book Pricing Strategies: A Text Analytics Approach

Zhang

Wang

et al. 2021

Mathematical Problems in Engineering

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Nowadays, electronic book vendors are increasingly proactive in trying to strategically capitalize on online big data generated by consumers. It will bring great profit for vendors if they can make the most of online reviews to figure out the impact of online data on the selling prices of e-books. In this paper, we complement an emerging body of research to explore how e-book prices could be affected by online information via analyzing the sheer volume of online data from e-book websites, namely, we first employ a domain ontology-based method to select the most discriminative features that may affect e-book prices. Then, the topic modeling method latent Dirichlet allocation and aspect-oriented sentiment analysis methods are applied as a supplement. Using the multiple regression method, we identify the key features that may have effects on the prices of e-books and give the related regression equation. In our results, some factors including paper book prices, paper book pages corresponding to the e-book, and e-book content have significant effects on the price of e-books. The managerial implication is that e-book firms can obtain a reference price for an e-book and may dynamically adjust the price to increase e-book sales according to our data analysis results.

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Section: Traditional Feature Extractionmentioning

confidence: 99%

The Effects of Online Information on E-Book Pricing Strategies: A Text Analytics Approach

Zhang

Wang

et al. 2021

Mathematical Problems in Engineering

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“…Similarly, semantics have also been frequently employed to predict consumer behavior. For instance, Li et al [16] explored consumer behavior analysis and prediction based on web mining and domain ontology, and introduced ontology to improve the timeliness and accuracy of commerce recommendations. Ashraf et al [17] proposed an ontology that reflects the semantics of consumer behavior, and used it to classify the knowledge and basic concepts of consumer behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

A Big Data-based Prediction Model for Purchase Decisions of Consumers on Cross-border E-commerce Platforms

Weizhe¹

2019

JESA

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In recent years, cross-border e-commerce platforms have become an important way for Chinese consumers to buy overseas products. This paper aims to find out the main influencing factors of the consumer behavior on cross-border e-commerce platforms, and predict the purchase decisions of food product on these platforms. Firstly, twelve factors that may affect the purchase decisions of consumers on cross-border e-commerce platforms were selected, and subjected to big data analysis. Seven of them were found to have a significant impact on consumer behavior on the said platforms. Next, a multilayer perceptron (MLP) was constructed to evaluate the importance of each influencing factor to the purchase decisions. The fuzzy weights of the most important factors were determined by an improved frequent-pattern (FP) growth algorithm. Finally, a purchase decision prediction model was established for consumers on cross-border e-commerce platforms, and successfully applied to predict the purchase decisions over 6 categories of products on such platforms.

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“…Forecasting the unemployment rate is one of the important elements allowing economic entities in the labor market to reduce the uncertainty resulting from the socio-economic situation of the country (Chakraborty et al, 2021;Błażejowski, 2009;Głuszczuk, 2016). In the face of changes, as well as the impact of structural factors, it becomes more and more complicated to make decisions regarding the day-to-day functioning of employees, employers and trade unions, as well as the actions of public authorities to solve the problem of unemployment (Li et al, 2014;Xu et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Unemployment Rate: Application of Selected Prediction Methods

Gostkowski¹,

Rokicki²

2021

ERSJ

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Purpose: Unemployment rate prediction has become critically significant, because it can be used by governments to make decision and design accurate policies. The paper's main objective is to compare the most significant predictive methods for modeling the unemployment rate. Design/Methodology/Approach: In this work, the selected predictive methods (naive method, regression model, ARIMA, Holt model and Winters model) were described, developed and compared using data collected by Central Statistical Office. Findings: The considered methods enable to predict the unemployment rate with high accuracy. The results of experiments allow to conclude that the most suited methods of forecasting the unemployment rate are the quadratic regression model and the Winters multiplicative model. Practical Implications: Forecasting the unemployment rate is one of the important elements in economy and presented methods can be easily used by labor market entities to predict and verify the situation in the market. Originality/Value: Forecasting the unemployment rate is an extremely difficult and demanding task, but on the other hand, it can be an effective tool that supports planning processes. The conducted research showed the quadratic regression model and the Winters multiplicative model provide high accuracy in terms of modeling the unemployment rate

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An ontology-based Web mining method for unemployment rate prediction

Cited by 43 publications

References 31 publications

The Effects of Online Information on E-Book Pricing Strategies: A Text Analytics Approach

The Effects of Online Information on E-Book Pricing Strategies: A Text Analytics Approach

A Big Data-based Prediction Model for Purchase Decisions of Consumers on Cross-border E-commerce Platforms

Forecasting the Unemployment Rate: Application of Selected Prediction Methods

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