Sentiment Analysis in Mobile Language Learning Apps Utilizing LSTM-GRU for Enhanced User Engagement and Personalized Feedback

Natural language processing's essential component, sentiment analysis, has drawn a lot of interest for its applications in various fields. To include Sentiment Analysis in mobile language learning apps, this study suggests a unique architecture that uses neural networks' Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) capabilities. The principal aim is to augment user involvement and provide tailored feedback, elevating the effectiveness of language acquisition endeavours. This paper highlights the shortcomings in the present mobile language learning systems and emphasizes the possible advantages of integrating Sentiment Analysis techniques through a thorough analysis of the literature. The suggested framework describes a thorough approach that includes gathering data, preprocessing, creating models, integrating them, and evaluating them. The creation of an effective LSTM-GRU model customized to the distinct properties of language acquisition data is given top priority. The sentiment analysis component must be smoothly integrated with current mobile apps during the deployment phase to provide scalability and instantaneous processing capabilities. The methodology is empirically evaluated by measuring model performance parameters in addition to user happiness and engagement. This work adds to the rapidly developing field of mobile language learning apps (MLLAs) by providing a data-driven method for improving user experiences via tailored feedback systems. The results are intended to guide further advancements in mobile application approach especially in the field of language learning. The proposed LSTM-GRU achieves 98% accuracy.