Classification of supervised deep learning models for COVID-19 tweets sentiment analysis
الباحث الأول:
Rusul Mohammed Alkhafaji
الباحثين الآخرين:
Shaymaa Awad Kadhim, Humam Adnan Sameer, Doaa hadi abid muslim
المجلة:
GSC Advanced Research and Reviews
تاريخ النشر:
28 يونيو، 2024
مختصر البحث:
Social media provided a successful management of human societies in light of global crises. Social media platforms havebeen considered the central authority in guiding society, receiving information and conducting business in manycountries during th…
Social media provided a successful management of human societies in light of global crises. Social media platforms havebeen considered the central authority in guiding society, receiving information and conducting business in manycountries during the COVID-19 pandemic period in March 2020. The social platform has seen an increase in use of 45%for public platforms and 35% for the use of messages. This study suggests An AI-based model for predicting thelikelihood of infection with COVID-19 through sentiment analysis and early detection using a Natural LanguageProcessing library with deep learning techniques CNN. The model performed improved the distinction between patientswho are 'positive' and patients who are 'natural' and unaffected are 'negative'. The performance of the model was testedusing publicly available databases on Twitter for the period from March 16, 2020 to April 14, 2020.The achievedaccuracy percentage was (~9 8.9% )) and based on the four measures Accuracy, Recall, Precision and F1-score.
