Technology Topic Identification and Trend Prediction of New Energy Vehicle Using LDA Modeling

Hu, Renjie; Wencong, Ma; Lin, Weiqiang; Chen, Xiude; Zu-chang, Zhong; Chu-hong, Zeng

doi:10.1155/2022/9373911

Cited by 16 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The patent citation network analysis method constructs networks between technical fields using citation relationships among patent documents. This approach calculates network attributes to unveil potential technical themes [19,20]. For example, Small et al [21] used the difference function to identify emerging technology topics based on direct citations and co-citations.…”

Section: Literature Reviewmentioning

confidence: 99%

Core Technology Topic Identification and Evolution Analysis Based on Patent Text Mining—A Case Study of Unmanned Ship

Lin,

Wang,

Yang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Accurate identification and evolutionary analysis of core technology topics within patent texts play a crucial role in enabling enterprises to discern the development trajectory of core technologies, optimize research and development (R&D) strategies, and foster technological innovation. Based on the perspective of time series dynamic analysis, this study uses the Latent Dirichlet Allocation (LDA) topic modeling and TF-IDF text vectorization methods to comprehensively mine and identify patent technology topics in the field of unmanned ships. This study deeply analyzes the dynamic evolution of unmanned ship technology topics from two aspects: the evolution of technology theme intensity and the evolution of technology theme content. We refine the development characteristics and future development directions of unmanned ship technology. The findings reveal two hot technologies, six growth technologies, and six declining technologies in unmanned ship technology. Furthermore, the analysis of technical topic evolution illustrates a pattern of fragmentation, inheritance, and integration. This study advances the methodologies used for identifying and analyzing patent technology topics and helps to grasp the development rules and evolutionary trends of core technologies. In addition, this paper has reference value for the research and practice of core technology topic identification and evolution analysis methods based on patent text mining.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Core Technology Topic Identification and Evolution Analysis Based on Patent Text Mining—A Case Study of Unmanned Ship

Lin,

Wang,

Yang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…It is different from other analyses in that it considers the flow of time [61], [62]. For patent data belonging to each technology topic, we converted it into time series data consisting of independent variables indicating the time point and dependent variables indicating the frequency to predict future trends [63], [64]. We used time series analysis to predict future patent application frequency trends for the converted data.…”

Section: ) Time Series Analysismentioning

confidence: 99%

Identifying the Technology Opportunities and the Technology Taxonomy for Railway Static Inverters With Patent Data Analytics

Lee,

Han,

Kim

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In this study, we identify and forecast promising railway static inverter technologies and the technology taxonomy using patent data analytics. To this end, we identify technology topics through LDA topic modeling and interpret them through N-gram analysis. We then derive detailed promising technology topics through time series analysis and technology mapping. Finally, we identify leading companies and research institutes in the field of promising technology topics through bibliography and social network analysis of patent applicants. Our study identified six technology topics, and fields related to converter technology were identified as detailed promising technologies. Japanese companies represented by Mitsubishi, Hitachi, and Toshiba are leading R&D in this field, while Chinese companies represented by CRRC have also secured technological competitiveness. Additionally, France's Alstom is technically competitive when considering only the centrality index. Our study will contribute to its use as a meaningful reference for the establishment of railway R&D and the development of promising railway static inverter technologies.

show abstract

“…Despite the guidance provided by the above model evaluation methods, issues such as mixed topics, illogical topics, and indistinguishable topics can still arise. To further ensure the effectiveness of modelling results, researchers are starting to improve traditional evaluation methods [43], proposing new metrics [41], prioritizing interpretability in model evaluation [48], and introducing expert opinion metrics such as homogeneity, completeness, and V-Measure [49] to guarantee the quality and reliability of topic generation. Some scholars also suggest the combined application of relevant methods and the establishment of evaluation mechanisms during model operation to dynamically adjust the optimal number of topics [10], thereby enhancing the flexibility of topic number selection.…”

Section: B Optimal Topics Number Selectionmentioning

confidence: 99%

Advancements of Artificial Intelligence Techniques in the Realm About Library and Information Subject—A Case Survey of Latent Dirichlet Allocation Method

Pan,

2023

IEEE Access

View full text Add to dashboard Cite

To investigate the advancements of artificial intelligence techniques in the realm of library and information subject, we have chosen the Latent Dirichlet Allocation method as a case study to explore its current study status and implementations. Traditional theme mining analyses utilize methods such as word frequency statistics, co-occurrence analysis, community detection, and citation analysis to capture external quantitative features of words or documents. In contrast, the Latent Dirichlet Allocation theme modelling method employs a three-layer Bayesian structure of document-topic-word to describe the themes of documents and the semantic relationships among words, enabling a better exploration of latent semantic information in text. This method plays a pivotal role in fine-grained knowledge extraction and analysis. We systematically review more than a decade of relevant literature in the realm about library and information subject. Through content analysis, we construct an analytical architecture for the implementation of the Latent Dirichlet Allocation method. This architecture, viewed from the perspective of the implementation process of Latent Dirichlet Allocation, comprehensively summarizes the core stages and technical challenges, including text pre-processing, model construction (i.e., theme model selection and optimal theme number determination), and model solving. Additionally, we provide a comprehensive overview of the current study status of the Latent Dirichlet Allocation method across various implementation domains, such as theme exploration, knowledge organization, academic evaluation, sentiment analysis, and recommendation study. Our findings indicate that the Latent Dirichlet Allocation method has formed a mature analytical process in the realm of library and information subject, with ongoing growth in study interest.

show abstract

Technology Topic Identification and Trend Prediction of New Energy Vehicle Using LDA Modeling

Cited by 16 publications

References 27 publications

Core Technology Topic Identification and Evolution Analysis Based on Patent Text Mining—A Case Study of Unmanned Ship

Core Technology Topic Identification and Evolution Analysis Based on Patent Text Mining—A Case Study of Unmanned Ship

Identifying the Technology Opportunities and the Technology Taxonomy for Railway Static Inverters With Patent Data Analytics

Advancements of Artificial Intelligence Techniques in the Realm About Library and Information Subject—A Case Survey of Latent Dirichlet Allocation Method

Contact Info

Product

Resources

About