A topic models based framework for detecting and forecasting emerging technologies

Xu, Shuo; Hao, Liyuan; Yang, Guancan; Lü, Kun; An, Xueli

doi:10.1016/j.techfore.2020.120366

Cited by 39 publications

(15 citation statements)

References 67 publications

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“…First, this study constructs an evolution analysis framework of interindustry technology linkage topics, which is composed of the Lingo algorithm and knowledge gene theory. Compared with the existing topic evolution analysis methods [49][50][51][52][53], the proposed analysis framework has the following advantages: in the aspect of analyzing topic dynamic change characteristics, this study embeds the similarity formula into the Lingo algorithm to calculate the similarity degree between adjacent time topics, which can effectively show the degree of inheritance or variation of topics over time. However, the existing methods [49][50][51][52][53] only extracted the topics in different time periods, and then counted the changes of the number of topics on different time nodes.…”

Section: Discussionmentioning

confidence: 99%

“…Compared with the existing topic evolution analysis methods [49][50][51][52][53], the proposed analysis framework has the following advantages: in the aspect of analyzing topic dynamic change characteristics, this study embeds the similarity formula into the Lingo algorithm to calculate the similarity degree between adjacent time topics, which can effectively show the degree of inheritance or variation of topics over time. However, the existing methods [49][50][51][52][53] only extracted the topics in different time periods, and then counted the changes of the number of topics on different time nodes. In essence, they only conducted a simple statistical analysis of several static time points of technical topics, and then reflect the changes of topics by investigating their differences at multiple static time points, without in-depth analysis of the inheritance or variation degree of topics in different periods, that lead to a very limited understanding of the evolution characteristics of topics.…”

Section: Discussionmentioning

confidence: 99%

“…Many scholars have proposed many topic evolution models using continuous time slices and variable topic analysis, such as the earlier topic over time (TOT) model [41], dynamic topic model (DTM) [42], dynamic mixture model (DMM) [43], and others [44][45][46][47][48]. These topic models and their extensions have been used in a wide variety of applications, such as assessments of enterprise ecosystems [49], cross-domain recommendations [50], forecasting emerging technologies [51], understanding user preferences [52], and patent information mining [53].…”

Section: Topic Evolution Analysismentioning

confidence: 99%

See 2 more Smart Citations

The Evolution of Interindustry Technology Linkage Topics and Its Analysis Framework in Three-Dimensional Printing Technology

Yang

Islam

Shi

et al. 2023

IEEE Trans. Eng. Manage.

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The mutual influence and complementarity of technologies between different industries are becoming increasingly prominent. Revealing the topic evolution of technology linkages between industries is the foundation for understanding the technological development trend of the industry. Although numerous works have focused on technology topic mining and its evolution characteristics, these works have not accurately represented the interindustry technology linkage, analyze the related topics and even ignored the technological development characteristics hidden in the topic evolution pathway. Since the Lingo algorithm fully considers the time-series characteristics of the topics, and the knowledge evolution theory can reveal three inherent characteristics in the evolution of knowledge topics, namely, "stability, heredity and variability," this paper aims to combine the Lingo algorithm and the knowledge evolution theory to analyze the topic evolution of interindustry technology linkages. Additionally, because 3D printing technology has significant interdisciplinary and cross-industry characteristics, a wide range of application fields, and various interindustry technology linkages, 3D printing technology is used for empirical analysis. The empirical results show that the key topics of interindustry technology linkages in 3D printing include model design, manufacturing methods, manufacturing equipment, manufacturing material and application. In addition, all these topics have the development feature of heredity.However, the topic of manufacturing materials presents significant variability, the topic of manufacturing methods has the strongest stability, and multiple subtopics of the five topics show variability and genetic intersection.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Topic Evolution Analysismentioning

confidence: 99%

See 1 more Smart Citation

The Evolution of Interindustry Technology Linkage Topics and Its Analysis Framework in Three-Dimensional Printing Technology

Yang

Islam

Shi

et al. 2023

IEEE Trans. Eng. Manage.

View full text Add to dashboard Cite

show abstract

“…It should be noted that when analyzing textual information about technology components or methods, it is convenient to include bigrams and/or trigrams in the analysis, since multi-word combinations can be indicative of problems and solutions (Heffernan & Teufel, 2018). Multiword based text mining results also improve the interpretation of text-mining analysis results, especially when dealing with complex topics such as technology solutions (Xu et al, 2021) or clinical risk factors (Sabra & Sabeeh, 2020), consequently, an approach based on n-grams seems to be the best choice when dealing with technically complex and context-dependent subjects.…”

Section: Literature Reviewmentioning

confidence: 99%

TeknoAssistant : a domain specific tech mining approach for technical problem-solving support

et al. 2022

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This paper presents TeknoAssistant, a domain-specific tech mining method for building a problem–solution conceptual network aimed at helping technicians from a particular field to find alternative tools and pathways to implement when confronted with a problem. We evaluate our approach using Natural Language Processing field, and propose a 2-g text mining process adapted for analyzing scientific publications. We rely on a combination of custom indicators with Stanford OpenIE SAO extractor to build a Bernoulli Naïve Bayes classifier which is trained by using domain-specific vocabulary provided by the TeknoAssistant user. The 2-g contained in the abstracts of a scientific publication dataset are classified in either “problem”, “solution” or “none” categories, and a problem–solution network is built, based on the co-occurrence of problems and solutions in the abstracts. We propose a combination of clustering technique, visualization and Social Network Analysis indicators for guiding a hypothetical user in a domain-specific problem solving process.

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“…By using combination of document influence model (DIM) and citation influence model (CIM), the growth, coherence, and influence indicators were extracted from papers and then were used to train the machine learning (ML) model to predict emerging topics (Xu et al, 2021b ). In their following work, DIM was replaced by the topical n-grams (TNG) model to exploit the potential topic models (Xu et al, 2021a ).…”

Section: Related Workmentioning

confidence: 99%

Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework

2022

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The detection of emerging trends is of great interest to many stakeholders such as government and industry. Previous research focused on the machine learning, network analysis and time series analysis based on the bibliometrics data and made a promising progress. However, these approaches inevitably have time delay problems. For the reason that leader papers of “emerging topics” share the similar characters with the “cover papers”, this study present a novel approach to translate the “emerging topics” detection to “cover paper” prediction. By using “AdaBoost model” and topic model, we construct a machine learning framework to imitate the top journal (chief) editor’s judgement to select cover paper from material science. The results of our prediction were validated by consulting with field experts. This approach was also suitable for the Nature, Science, and Cell journals.

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A topic models based framework for detecting and forecasting emerging technologies

Cited by 39 publications

References 67 publications

The Evolution of Interindustry Technology Linkage Topics and Its Analysis Framework in Three-Dimensional Printing Technology

The Evolution of Interindustry Technology Linkage Topics and Its Analysis Framework in Three-Dimensional Printing Technology

TeknoAssistant : a domain specific tech mining approach for technical problem-solving support

Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework

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