Sentiment analysis on medical discharge summaries

Abstract

newline ABSTRACT newlineText extraction and segmentation techniques are essential for transforming unstructured data into actionable insights, a critical need in various industries. This includes applications in computer science, natural language processing, machine learning, and information science. This abstract combines the general overview of text extraction and segmentation with a specific focus on sentiment text classification applied to healthcare data. Sentiment text classification is crucial for extracting valuable insights from unstructured discharge summaries, which often contain subjective sentiments related to patient experiences and overall satisfaction. This research leverages state-of-the-art natural language processing tools LSTM, BERT and libraries such as Pattern, SentiWordNet and proposed a novel library approach to classify sentiments into positive, negative, or neutral categories in discharge summaries and medical reports.The sentiment analysis process involves preprocessing steps, including text cleaning, tokenization, and lemmatization, ensuring accuracy and effectiveness. By successfully implementing sentiment text classification, this research aims to extract valuable insights from patient sentiments. Analyzing sentiments in discharge summaries allows healthcare providers to estimate patient satisfaction and identify areas for service improvement, ultimately contributing to the enhancement of overall patient care quality. The research showcases the interdisciplinary relevance of text extraction and sentiment analysis in addressing real-world problems and providing practical solutions for healthcare providers. newline