Machine Learning Method Employed for the Objective of Identifying Text on Tweet Dataset

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When it comes to training ML systems, internet-based data is invaluable. Despite the difficulty in collecting this information, teams of experts from academic institutions and research labs have created publicly accessible databases. Twitter and other social media platforms provided large quantities of useful information throughout the pandemic, which was used to evaluate healthcare decisions. In order to forecast illness prevalence and offer early warnings, we suggest analysing user attitudes by using efficient supervised machine learning algorithms. The gathered tweets were sorted into positive, negative, and neutral categories for preprocessing. Hybrid feature extraction is the innovative aspect of our work; we used it to correctly describe posts by combining syntactic features (TF-IDF) and semantic elements (FastText and Glove), which in turn improved classification. The experimental findings suggest that when using Naive Bayes, the combination of FastText and TF-IDF achieves the best results.