Drug Sentiment Analysis: A Comprehensive Study Using Regression Models and Natural Language Processing

Sentiment analysis is a critical technology in the era of ubiquitous digital communication that helps extract user opinions from large volumes of textual data. Our approach combines the Random Forest, XGBoost, and linear regression models with sentiment analysis and feature engineering via Term Frequency-Inverse Document Frequency (TF-IDF). The study provides a comprehensive understanding of drug-related sentiments by utilizing a diverse dataset that includes user-generated reviews and associated metadata. Moreover, the examination integrates medication-specific attributes and normalized counts, augmenting the level of detail in sentiment insights. Our technological approach shows effectiveness in capturing complex emotions present in online drug discussion user discourse. This study tackles the challenging task of extracting sentiments from drug-related online discussions while also contributing to the growing field of sentiment analysis. The results clarify how sophisticated regression models can be used to extract minute sentiment nuances, setting the stage for further advancements in pharmaceutical informatics. This paper serves as a comprehensive guide for researchers, providing invaluable insights into the utilization of advanced methodologies in sentiment analysis within the realm of pharmaceuticals.