A topic modeling based bibliometric exploration of hydropower research

Jiang, Hanchen; Qiang, Maoshan; Lin, Peng

doi:10.1016/j.rser.2015.12.194

Cited by 127 publications

(98 citation statements)

References 56 publications

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“…For example, it makes bibliometric analysis adaptive to large-scale textual data beyond scientific publications. In addition, it can reflect the practical issues in the whole life cycle of research development, since the data are obtained using scientific methods [16]. Integrating the cutting-edge text mining approaches with the time-honoured bibliometric approach forms a robust empirical framework which situates fine-grained discursive results in the large textual data sources.…”

Section: What Are the Distributions Of These Topics Across Different mentioning

confidence: 99%

Topics and trends in artificial intelligence assisted human brain research

Chen

Cheng

et al. 2020

PLoS ONE

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Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AIassisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AIassisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AIassisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.

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Section: What Are the Distributions Of These Topics Across Different mentioning

confidence: 99%

Topics and trends in artificial intelligence assisted human brain research

Chen

Cheng

et al. 2020

PLoS ONE

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“…There are a handful of studies on the application of topic modelling in energy research. For example, Jiang, Qiang, & Lin, (2016) applied topic modelling on a bibliometric dataset on hydropower research. They established 29 topics that described the intellectual architecture of hydropower research and found that an interdisciplinary lens in hydropower research is needed for higher-level policy benefits.…”

Section: Topic Modelling In Humanities and Social Science Researchmentioning

confidence: 99%

A nested computational social science approach for deep-narrative analysis in energy policy research

Debnath¹,

Darby²,

Bardhan³

et al. 2020

Preprint

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Text-based data sources like narratives and stories have become increasingly popular as critical insight generator in energy research and social science. However, their implications in policy application usually remain superficial and fail to fully exploit state-of-the-art resources which digital era holds for text analysis. This paper illustrates the potential of deep-narrative analysis in energy policy research using text analysis tools from the cutting-edge domain of computational social sciences, notably topic modelling. We argue that a nested application of topic modelling and grounded theory in narrative analysis promises advances in areas where manual-coding driven narrative analysis has traditionally struggled with directionality biases, scaling, systematisation and repeatability. The nested application of the topic model and the grounded theory goes beyond the frequentist approach of narrative analysis and introduces insight generation capabilities based on the probability distribution of words and topics in a text corpus. In this manner, our proposed methodology deconstructs the corpus and enables the analyst to answer research questions based on the foundational element of the text data structure. We verify the theoretical and epistemological fit of the proposed nested methodology through a meta-analysis of a state-of-the-art bibliographic database on energy policy and computational social science. We find that the nested application contributes to the literature gap on the need for multidisciplinary polyvalence methodologies that can systematically include qualitative evidence into policymaking.

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“…Implementing detailed mining of literatures content can help us obtain deeper insight into technology development [11]. To effectively process and analyze the content of scientific literatures, machine learning method based on topic models have been proposed and widely used in technical foresight tasks in recent years [26][27][28][29][30][31][32]. The topic models in above works can be employed to mine a large number of literature contents to obtain latent intellectual structures of technological focuses related with a specific technology domain, which can model the complex inherent heterogeneous technology structures.…”

Section: Raju Et Al Carried Out An Qualitative Literature Study To Dmentioning

confidence: 99%

Exploring the Future Development of Research, Technology and Business of Machine Tool Domain in New-Generation Information Technology Environment Based on Machine Learning

Chen¹,

Zhang²,

Zhou³

et al. 2019

Preprint

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The combination of new-generation information technology and manufacturing technology has resulted in major and profound impact on future development paradigm of manufacturing. It is challenging for existing methods to conduct a multidimensional trend exploration related to machine tool domain, which is the basis of virtually everything in manufacturing. In this paper, we proposed an integrating approach framework combined topic models, bibliometric, trend analysis and patent analysis to mine insightful information about future development from multi-source data related to machine tool, such as papers, grants, patents and news. Specifically, papers and grants provided two different perspectives to explore the current focuses and future trends in machine tool research. Furthermore, the future technology development of machine tool was investigated through patents analysis. Finally, news related to machine tool industry in recent years was analyzed to analyze future machine tool business mode. The integration of the above various analytical methods and the multi-dimensional mining of literatures enabled the analysis of the future development of machine tool domain systematically from multi-perspectives which include research, technology development and industry. The conclusions obtained in this paper is beneficial to different communities of machine tool in terms of determining the research directions for researchers, identifying industry opportunities for corporations and developing reasonable industry policy for policy makers.

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A topic modeling based bibliometric exploration of hydropower research

Cited by 127 publications

References 56 publications

Topics and trends in artificial intelligence assisted human brain research

Topics and trends in artificial intelligence assisted human brain research

A nested computational social science approach for deep-narrative analysis in energy policy research

Exploring the Future Development of Research, Technology and Business of Machine Tool Domain in New-Generation Information Technology Environment Based on Machine Learning

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