期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：5
DOI：10.14569/IJACSA.2022.0130507
语种：English
出版社：Science and Information Society (SAI)
摘要：The analysis of the unstructured text has become a challenge for the community dedicated to natural language processing (NLP) and Machine Learning (ML). This paper aims to describe the potential of the most used NLP techniques and ML algorithms to address various problems afflicting our society. Several original articles were reviewed and published in SCOPUS during 2021. The applied approach was retrospective, transversal and descriptive. The data collected were entered into the SPSS statistical software v25 and among the findings, it was determined that the most used NLP technique was the Term frequency - Inverse document frequency (TF-IDF), while the most used supervised learning algorithm was the Support Vector Machines (SVM). Likewise, the predominant deep learning algorithm was Long Short-Term Memory (LSTM). This research aims to support experts and those starting in research to identify the most used algorithms of NLP and ML.
关键词：Artificial intelligence; natural language processing; machine learning; unstructured text analysis
