Deep Learning-based Sentiment Analysis of User Generated Reviews of Various AI Powered Mobile Applications

AI powered apps offer numerous benefits and positive impacts on various aspects of daily life, offering innovative solutions, personalized experiences, and enhanced efficiency across diverse domains. Using these AI powered apps (ChatGPT, Imagine, Replica, Bing, Alexa, Lensa, Arta, Ask AI, Answer. AI, and Write Cream AI.) are very easy, as these apps are commonly available in the Google Play Store. This research delves into the analysis of sentiment within user reviews of AI Google Play Store apps. By using 122231 data points that have been classified as neutral, negative, and positive feelings, our aim is to apply advanced deep learning algorithms to determine patterns in sentiment expression. Through meticulous data preprocessing, the unstructured dataset is transformed into a well-structured format, enabling insightful analysis. Employing six distinct deep learning methods on the categorized dataset, including CNN (90.53%), LSTM (91.97%), BiLSTM (91.67%), GRU (91.44%), BiGRU (91.34%), and CNN+LSTM (92.1%), this study has achieved notable accuracy rates. Particularly, the pre-trained CNN+LSTM model emerges as the best performer, attaining the highest accuracy of 92.1%.