State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
Sci Total Environ. 2019 Jul 10;673:402-413. doi: 10.1016/j.scitotenv.2019.04.051. Epub 2019 Apr 7.
The objective of this study is to apply natural language processing to identifying innovative technology trends related to food waste treatment, biogas, and anaerobic digestion. The methodology used involved analyzing large volumes of text data mined from 3186 patents related to these three fields. Latent Dirichlet Allocation and the perplexity method were used to identify the main topics which the patent corpora were comprised of and which technological concepts were most associated with each topic. In addition, term frequency-inverse document frequency (TF-IDF) was used to gauge the "emergingness" of certain technical concepts across the patent corpora in various years. The key results were as follows: (1) perplexity computations showed that a 20 topic models were feasible for these patent corpora; (2) topics were identified, providing an accurate picture of the patenting landscape in the analyzed fields; (3) TF-IDF analysis on unigrams, bigrams, and trigrams, supplemented with network graph analysis, revealed emerging technology trends in each year. This study has important implications for governments who need to decide where to invest resources in anaerobic food waste treatment.
本研究旨在应用自然语言处理技术来识别与食物垃圾处理、沼气和厌氧消化相关的创新技术趋势。所采用的方法涉及从与这三个领域相关的 3186 项专利中挖掘大量文本数据并进行分析。采用潜在狄利克雷分配和困惑度方法来识别专利文献的主要主题以及与每个主题关联最紧密的技术概念。此外，还使用术语频率-逆文档频率（TF-IDF）来衡量各年专利文献中某些技术概念的“新兴度”。主要结果如下：（1）困惑度计算表明，对于这些专利文献，20 个主题模型是可行的；（2）确定了主题，为所分析领域的专利格局提供了准确的描述；（3）对单字、双字和三字的 TF-IDF 分析，辅以网络图形分析，揭示了每年的新兴技术趋势。本研究对于需要决定在厌氧食物垃圾处理方面投资资源的政府具有重要意义。
