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A Text Mining Approach in Patents and Papers: Analyzing Wind and Solar Energy Trends
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 37 |
| Year of Publication: 2020 |
| Authors: Cleber Gustavo Dias, Alaydes Mikaelle De Morais |
10.5120/ijca2020920947
|
Cleber Gustavo Dias, Alaydes Mikaelle De Morais . A Text Mining Approach in Patents and Papers: Analyzing Wind and Solar Energy Trends. International Journal of Computer Applications. 175, 37 ( Dec 2020), 1-12. DOI=10.5120/ijca2020920947
Abstract
Renewable energies have become extremely relevant assets for many countries during the past years. More particularly, some research institutions and companies around the world have invested efforts and significant resources to foster new technical solutions and scientific discoveries in two of the most important areas in renewable developments, such as wind and solar energy. Therefore, this paper presents a technological trajectory analysis in both fields, using text-mining techniques in granted patent and patent application documents from 2010 until 2019 and papers published by IEEE Xplore Digital Library, considering the same period. The IEEE library has more than 5 million items and the World Intellectual Property Organization database has around 84 million patent documents. The Rstudio software was used for text-mining purposes in the abstracts and other text information for both repositories. The present research has processed 68064 patents documents for wind energy and 59224 patents documents for solar energy. Moreover, more than 13158 papers have been processed for wind and solar energy during the aforementioned period. The results have shown not only the state of the art about the technologies developed by inventors and patent applicants, but also potential trends of the distinct stakeholders on each type of renewable energy.
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