The Comparative Study of Machine Learning Algorithms for Sentiment Analysis in Multimodal Medical Data: Comparative Study of Machine Learning Algorithms for Sentiment Analysis in Multimodal Medical Data

Hafiz_Bilal Ghani; Muhammad Asif; Muhammad Azam Zia

Authors

Hafiz_Bilal Ghani University of Agriculture Faisalabad
Muhammad Asif Information Technology University
Muhammad Azam Zia Information Technology University

Keywords:

Sentiment Analysis, Machine Learning, Natural Language Processing, Confusion Matrix

Abstract

Sentiment analysis, a part of data mining, uses Natural Language Processing (NLP) to understand how people feel about certain topics or individuals. It focuses on the context and polarity of information, measuring public opinions from unstructured sources like social networks and healthcare websites. By extracting useful insights from this unstructured data, healthcare professionals can improve patient care, make accurate diagnoses, and provide personalized treatments. Machine learning (ML) plays a key role in this process. ML techniques like logistic regression, decision trees, and Naive Bayes have proven effective in tasks such as sentiment analysis and named entity recognition in medical data. The goal of ML is to create algorithms that enhance data processing and decision-making by identifying patterns that might be overlooked by humans. In this study, we compare the performance of three common ML models—(a) Logistic Regression, (b) Decision Tree, and (c) Naive Bayes—for sentiment analysis on medical image captions. The Radiology Objects in Context (ROCO) multimodal image and caption dataset was used for this NLP task. Caption pre-processing is done using filtering methods to improve text quality, followed by sentiment classification using pre-trained ML models. This comparison sheds light on the effectiveness of these algorithms in performing sentiment analysis in clinical settings.

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