Semantic search for public opinions on urban affairs: A probabilistic topic modeling-based approach

Ma, Baojun; Zhang, Nan; Liu, Guannan; Li, Liangqiang; Yuan, Hua

doi:10.1016/j.ipm.2015.10.004

Cited by 46 publications

(16 citation statements)

References 49 publications

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“…Instead, we needed to rely on mixed-membership models (Airoldi, Blei, Fienberg, & Xing, 2008) where the assumption that a unit belongs to a single cluster is violated (Airoldi, Blei, Erosheva, & Fienberg, 2014). For the sake of identifying skill sets within job posts, we decided to adopt the mixed-membership model Latent Dirichlet Allocation, LDA (Blei, 2012), which has proven to work effectively at analyzing user-generated content like job posts (Ma, Zhang, Liu, Li, & Yuan, 2016).…”

Section: Identification Of Skill Setsmentioning

confidence: 99%

Human resources for Big Data professions: A systematic classification of job roles and required skill sets

Mauro

Greco

Grimaldi

et al. 2018

Information Processing & Management

258

178

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The rapid expansion of Big Data Analytics is forcing companies to rethink their Human Resource (HR) needs. However, at the same time, it is unclear which types of job roles and skills constitute this area. To this end, this study pursues to drive clarity across the heterogeneous nature of skills required in Big Data professions, by analyzing a large amount of real-world job posts published online. More precisely we: 1) identify four Big Data 'job families'; 2) recognize nine homogeneous groups of Big Data skills (skill sets) that are being demanded by companies; 3) characterize each job family with the appropriate level of competence required within each Big Data skill set. We propose a novel, semi-automated, fully replicable, analytical methodology based on a combination of machine learning algorithms and expert judgement. Our analysis leverages a significant amount of online job posts, obtained through web scraping, to generate an intelligible classification of job roles and skill sets. The results can support business leaders and HR managers in establishing clear strategies for the acquisition and the development of the right skills needed to leverage Big Data at best. Moreover, the structured classification of job families and skill sets will help establish a common dictionary to be used by HR recruiters and education providers, so that supply and demand can more effectively meet in the job marketplace.

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Section: Identification Of Skill Setsmentioning

confidence: 99%

Human resources for Big Data professions: A systematic classification of job roles and required skill sets

Mauro

Greco

Grimaldi

et al. 2018

Information Processing & Management

258

178

View full text Add to dashboard Cite

show abstract

“…The study of temporal dynamics of controversial themes has been addressed in the literature, especially in the context of online social network analysis (Yardi and Boyd, 2010;Smith et al, 2013). In addition to analyzing the changes of discussion and controversy within and between (1-year) participatory budgeting processes, we envision as a promising research line linking and comparing citizen participation in ePB platforms and in online social networks, such as Twitter (Ma et al, 2016;Alizadeh et al, 2019;Driss et al, 2019;Vargas-Calderón and Camargo, 2019). This may raise enriched insights about the characteristics of participants, proposals and discussed issues, as well as the types and levels of controversy.…”

Section: Discussionmentioning

confidence: 99%

Exploiting Open Data to analyze discussion and controversy in online citizen participation

Cantador

Cortés-Cediel

Fernández

2020

Information Processing & Management

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In this paper we propose a computational approach that applies data mining techniques to analyze the citizen participation recorded in an online digital platform. Differently to previous work, the approach exploits external knowledge extracted from Open Government Data for processing the citizens' proposals and debates of the platform, enabling to characterize targeted issues and problems, and analyze the levels of discussion, support and controversy raised by the proposals. As a result of our analysis, we derive a number of insights and conclusions of interest and value for both citizens and government stakeholders in decision and policy making tasks. Among others, we show that proposals targeting issues that affect large majorities tend to be supported by citizens and ultimately implemented by the city council, but leave aside other very important issues affecting minority groups. Our study reveals that most controversial, likely relevant, problems do not always receive sufficient attention in e-participation. Moreover, it identifies several types of controversy, related to ideological and socioeconomic factors and political attitudes.

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“…at a same proportion. To ensure that the degree of interest in learning resources is increased when both Fresh and Wnormalized are higher, we usually express this interest degree as the average of the two "as stated in [10]", see formula (5) for specific calculation:…”

Section: Methodsmentioning

confidence: 99%

Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis

Zhang

Wang

2017

Int. J. Emerg. Technol. Learn.

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Abstract-This paper explores the personalized approach of the public opinion cluster analysis for learning resources based on the server-side predetermined analysis, in order to introduce the personalized learning resource recommender into the traditional online instruction. In allusion to further validation on its implementation, the fuzzy aggregation of learning resources is mined up based on the proposed WRTC algorithm. The personalized learning resource recommender mechanism is then described. In the end, the common evaluation parameters in the personalized recommender model are applied in the evaluation on the system performance. The experiment is carried out with learner's access data online to validate whether the algorithm and the model indicators are effective for the purpose of improving the precision and coverage of learning resources.

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Semantic search for public opinions on urban affairs: A probabilistic topic modeling-based approach

Cited by 46 publications

References 49 publications

Human resources for Big Data professions: A systematic classification of job roles and required skill sets

Human resources for Big Data professions: A systematic classification of job roles and required skill sets

Exploiting Open Data to analyze discussion and controversy in online citizen participation

Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis

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