Initial Progress of Identification of the Appropriate NLP Technique for Content Evaluation in Textual Conversations of People Infected by Sars-Cov-2
DOI:
https://doi.org/10.53591/easi.v2i3.2488Keywords:
Python, Google Collab, NLP, LSTMAbstract
When Covid-19 became a pandemic on March 2020, an urgent need arose for reliable info and advice, so Virtual Assistants were created to help teach the public how to avoid the Alpha variant. But when new variants like Beta, Delta, and Omicron appeared with different symptoms, they caused new waves of infections and deaths. To tackle this, a Natural Language Processing prototype was created to analyze experiences of 4422 people, who had been infected in Ecuador, and to detect which symptoms were most common in their conversations. This study prompted the creation of the NLP prototype, using Python language, the Google Collab platform, two combinations of NLP techniques were considered, measuring results through quality metrics, accuracy, Recall, F1, finding that the most appropriate combination of techniques of the two tested the one that gave the highest effectiveness for a Multi-Label classifier model, including Stop Word, Tokenization, Stemming with LSTM (Long Short-Term Memory) classifier, as a first advance of the study.
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Copyright (c) 2023 Ivan L. Acosta-Guzmán, Eleanor A. Varela-Tapia, Alexandra E. Piza-Guale, Nory X. Acosta-Guzmán, Christopher I. Acosta Varela
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