Drug Recommendations Using a Reviews and Sentiment Analysis by RNN

Sentiment analysis plays a crucial role in understanding the opinions and attitudes expressed in textual data. This paper explores the utilization of two distinct approaches, Recurrent Neural Networks (RNNs) and Cellular Automata (CA), for recommending drugs based on sentiment analysis of user reviews. Recurrent Neural Networks (RNNs) have emerged as a powerful tool for analyz ing sequential data. In the context of sentiment analysis, RNNs excel at capturing contextual information and dependencies between words within a sentence. By training an RNN on a labeled dataset of drug reviews, sentiment patterns can be learned, enabling the model to predict the sentiment associated with unseen reviews. Cellular Automata (CA) offer an alternative approach to sentiment analysis. CA are discrete systems where cells transition between states based on local interactions with neighboring cells. Applying CA to sentiment analysis involves repre senting each word or phrase in a review as a cell, and defining rules that govern sentiment state transitions based on neighboring cells’ sentiments. By iteratively updating the cellular automaton over multiple time steps, sentiment dynamics within the text corpus can be modeled. RNNs are particularly adept at capturing long-term dependencies and contextual nuances within a text sequence. Conversely, CA provide a spatially extended framework that can capture spatial dependencies between words. We propose a hybrid method RNN-CA-DR using both of these methods for developing a robust and accurate classifier for drug recommendation. The developed classifier has reported an accuracy of 91.23% and outperformed few base line models when tested with various parameters F1 Score, precision and recall.