The most researched and used biodegradable aliphatic polyester worldwide is poly (lactic acid) -PLA. Due to its inherent properties, PLA is a leading biomaterial for several and numerous applications and the most promising biopolymer capable of replacing conventional petroleum-derived polymers. Besides, PLA is also one of the most promising candidates for new developments in the traditional sectors such as packaging and the automotive industry, in the electronics industry, and the biomedical area. To verify the importance of drug release in the various applications of PLA, a text mining of the last ten years was carried out. The text-mining tool was used to identify this polymer's main applications in the last ten years, allowing us to draw a map year by year based on correlation analysis. Firstly, an analysis was made of the main applications of PLA in scientific research, by searching for "PLA", in the ScienceDirect scientific base (https://www.sciencedirect.com/) and using the online tool Voyant Tools. Some words related to drug release were selected, checking how many times they were mentioned over the years. In this way, it was possible to see which years were the most and least used PLA in drug release. It was not possible to observe a gradual increase or decrease in this polymer's use, for this purpose, over time. The text mining presented here proved to be an efficient and fast way to observe the desired theme.
Many polymers are used as drug carriers for controlled delivery because of its many advantages. To find the most used polymers for this purpose in recent years, we chose to use Text Mining as an evaluation tool. Text Mining tool was precious in this work to identify the main polymers that contributed to drug delivery, allowing us to draw a map year by year based on correlation analyzes. Firstly, an analysis was made on what was most researched per year, and then what were the most cited polymers. Then the most mentioned polymers were separated and organized to observe which year it was more or less cited. It was found with this work that none of the polymers showed only positive correlations in all years. Besides, polymers did not reveal individual growth in their use over the years. The Text Mining presented here proved to be an efficient and quick way to observe the desired theme and open up possibilities for research in different fields.
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