The Effect of Natural Language Processing on the Analysis of Unstructured Text: A Systematic Review

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.

@article{Roldan-Baluis2022,
  title     = {The Effect of Natural Language Processing on the Analysis of Unstructured Text: A Systematic Review},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {5},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Walter Luis Roldan-Baluis and Noel Alcas Zapata and Maria Soledad Manaccasa Vasquez},
  doi       = {10.14569/IJACSA.2022.0130507},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130507}
}