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Translator Data Pre-processing Gram Feature Algorithmic Model (TDGA) for Enhancing Classifier Accuracy in the Healthcare Domain

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Abstract

Coronavirus COVID-19 has been spreading like wildfire all over the world since the year 2019. Nowadays, everyone is interacting with social media on a regular basis. In this study, the primary objective is to examine how Twitter users feel about COVID-19’s social life and to conclude their opinions. The polarity of feelings is determined by machine-learning classifiers that use popular words such as Coronavirus and COVID-19 to identify them. In addition, viruses such as BA-4 and BA-5 types of omicrons are spreading widely all over the globe. In order to prevent themselves from these types of viruses, the public needs to know the exact sentiment of the current social life problem. Using the newly reported topics/themes/issues and the associated sentiments from various factors, the COVID-19 pandemic can be better understood. A large dataset of tweets conveying information regarding COVID-19 is analyzed in this article, in particular, the credibility of the information shared on Twitter. It was evaluated using unigrams, bigrams, and trigrams with different parameters such as f1 score, precision, recall and compared against accuracy using machine-learning techniques such as Support Vector Machine (SVM), Naive Bayes (NB), and Logistic Regression (LR). The model TDGA (Translator Data pre-processing Gram feature Algorithmic model) performs well in individuals’ assessments of COVID-19 and benchmark COVID datasets, with a maximum efficiency of 86%.

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Data availability

Dataset used in this journal is Benchmark twitter dataset taken form IEEE Repository.

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Authors and Affiliations

  1. Department of Computer Science, Bishop Heber College (Autonomous) (Affiliated to Bharathidasan University), Trichy, Tamil Nadu, 620017, India

    A. Sathya

  2. Department of Computer Applications, Bishop Heber College (Autonomous), (Affiliated to Bharathidasan University), Trichy, Tamil Nadu, 620017, India

    M. S. Mythili

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  1. A. Sathya
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This article is part of the topical collection “Industrial IoT and Cyber-Physical Systems” guest edited by Arun K Somani, Seeram Ramakrishnan, Anil Chaudhary and Mehul Mahrishi.

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Sathya, A., Mythili, M.S. Translator Data Pre-processing Gram Feature Algorithmic Model (TDGA) for Enhancing Classifier Accuracy in the Healthcare Domain. SN COMPUT. SCI. 4, 542 (2023). https://doi.org/10.1007/s42979-023-01895-x

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