Authors : Archana Kesavan; Dr. Pasupathi Perumalsamy; Kowsalya Sakthivel

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/3m6aehm6

Scribd : https://tinyurl.com/mt429efh

DOI : https://doi.org/10.38124/ijisrt/26jun2046

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Healthcare educational videos play an important role in spreading medical information and increasing public awareness. Viewer comments on these videos provide valuable insights into audience opinions, satisfaction, and engagement. This study uses sentiment analysis to evaluate patient engagement through healthcare educational video comments. A Kaggle dataset containing 3,002 comments, including 1,500 positive and 1,502 negative comments, was used for the analysis. The text was preprocessed using lowercasing, punctuation removal, tokenization, stop-word removal, and lemmatization. TFIDF with unigram and bigram features was applied to convert the text into numerical form. The dataset was split into 80 percent training data and 20 percent testing data using a stratified sampling method. Three machine learning models— Logistic Regression, Multinomial Naïve Bayes, and Linear Support Vector Machine (SVM)—were trained and compared. Logistic Regression achieved the highest accuracy of 90.37 percent, followed by Multinomial Naïve Bayes (80.20percent) and Linear SVM (80.03 percent). The results show that Logistic Regression with TF-IDF is an effective and efficient method for healthcare sentiment analysis. The proposed framework helps understand patient engagement and supports the improvement of healthcare educational content through data-driven insights.

Keywords : Sentiment Analysis; Healthcare Educational Videos; Patient Engagement; Machine Learning; Logistic Regression; TFIDF; Natural Language Processing.

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