Introduction <p>Sentimental analysis is a vital component in understanding users’ opinions and emotions towards various products and services. In the context of healthcare apps, analyzing user reviews can provide valuable insights into user satisfaction, identify potential issues, and guide app developers in making data-driven improvements. Through the effective analysis and interpretation of sentiment in user reviews using deep learning techniques, this research aims to close the gap and give app developers ideas for bettering user experience and healthcare results.</p> Purpose <p>The research begins by collecting a sizable dataset of user reviews from the Google Playstore related to healthcare applications. The reason behind applying sentiment analysis to healthcare app playstore user reviews using deep learning techniques lies in the potential benefits it offers to both users and healthcare providers. Understanding the tone used in user evaluations can offer insightful information about the functionality, usability, and overall user experience of the programme. The reviews are preprocessed, including text normalisation, tokenization, and removing stop words, to ensure uniformity and to eliminate noise from the data.</p> Techniques <p>The hybrid deep learning model is designed and implemented for sentiment examination on healthcare playstore user reviews. In order to capture sequential dependencies and extract valuable features from textual data, several different types of neural networks are investigated, enhancing the accuracy, efficiency, and overall effectiveness of understanding user sentiments. This, in turn, can lead to improved healthcare app development and better user experiences.</p> Result <p>With an accuracy rate of 92%, the suggested hybrid LSTM-CNN model performs better than other models.</p> Conclusion <p>In sentiment analysis, hybrid models perform better than single models.</p>