Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia

Research Article

Using LDA for Innovation Topic of Technology : Quantum Dots Patent Analysis

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  • @INPROCEEDINGS{10.4108/eai.2-8-2019.2290336,
        author={Nurmitra Sari  Purba and Rani  Nooraeni},
        title={Using LDA for Innovation Topic of Technology : Quantum Dots Patent Analysis},
        proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia},
        publisher={EAI},
        proceedings_a={ICSA},
        year={2020},
        month={1},
        keywords={lda noun phrases extraction patent map quantum dots text mining},
        doi={10.4108/eai.2-8-2019.2290336}
    }
    
  • Nurmitra Sari Purba
    Rani Nooraeni
    Year: 2020
    Using LDA for Innovation Topic of Technology : Quantum Dots Patent Analysis
    ICSA
    EAI
    DOI: 10.4108/eai.2-8-2019.2290336
Nurmitra Sari Purba1,*, Rani Nooraeni1
  • 1: STIS, Politechnic of Statistics,,East Jakarta, 13330, Indonesia
*Contact email: 15.8804@stis.ac.id

Abstract

This study seeks to explore information about one of nanotechnology, quantum dots (QDs), through analysis of patent information. QDs patent documents obtained from the United States international patent database, the USPTO, use web scraping. In total, 3914 patents from 1988 to 2016 were taken and archived for analysis. This paper discusses how to apply Latent Dirichlet Allocation (LDA), a topic model, in a trend analysis methodology that exploits patent information. After the text preprocessing and transformation, the number of topics is decided using the log likelihood value. Then LDA model is used for identifying underlying topic structures based on latent relationships of technological words extracted. We extracted words from 6 relevant topics and showed that these topics are highly meaningful in explaining technology applications of QDs.