# Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models
###### Abstract  
The rapid and wide proliferation of mobile phones has led to accelerated demand for mobile applications (apps). Consequently, a large number of mobile apps have been developed and deployed on the Google and Apple Play stores. Calling apps hold special importance in this regard by offering the services of sharing messages, making video calls, and sending audio messages, free of cost. Although each app has its own set of features, different apps can provide higher levels of satisfaction for the user, and aspect analysis is often overlooked by existing studies. This study presents an aspect-level analysis of IMO, Skype, Telegram, WeChat, and WhatsApp regarding the services offered for the account, app, call, message, update, video, and working features. A large collected dataset from the Google Play store is utilized for aspect extraction and analysis using the Latent Dirichlet Allocation (LDA) model. Apps are analyzed using LDA-extracted aspects and recommended regarding users' priorities of call, message, and video requirements. Sentiment analysis is adopted to analyze user sentiments regarding apps as well as to aid in the aspect analysis. For sentiment analysis, a novel ensemble model of a gated recurrent unit and convolutional neural network is presented, which obtains a 94% accuracy score. app recommendation; best app; sentiment analysis; deep learning; aspect-level analysis Article Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models [PERSON], [PERSON]  
are introducing their apps for different functionalities. Companies attempt to make apps efficient, easy-to-use, and error-free to attract more users. From this perspective, they continue improving the app-offered services and the user experience, and they add features and functionalities. For improving the app functionality and user experience, user feedback is analyzed, which is often in the form of posted reviews. Sentiment analysis of such reviews can provide insight into the nature of the problems users face, and appropriate corrections and improvements can be made accordingly. For the most part, existing studies recommend calling apps using the review sentiments alone and do not consider particular aspects, such as a message service, video call service, etc. This study considers such aspects to recommend a particular app based on the sentiments of users. App reviews are used to determine which calling apps are better in functionality with respect to user sentiments. Calling apps are widely used by people all around the world as they provide an inexpensive and easy method of communication among users connected to the internet. Calling apps provide different features to their users, such as sending messages, making calls, and video calls. Calling apps are considered an important factor in generating huge revenue. For example, the stats for WhatsApp, one of the widely used calling apps, are given in Table 1[3]. In addition, a large number of calling apps are available in the market, such as Skype, IMO, and WeChat, which provide services all around the world with different features. For improving current services and offering new services, apps request feedback from users in the form of reviews. Users can provide feedback through the rating in the form of stars, as well as write reviews in the form of text. Users' reviews are considered to be an important benchmark as they can help companies to improve their apps according to the sentiments and grow app users as well as earn more profit. Keeping in view the importance of user sentiments, the analysis of such sentiments is a task of great value. Many researchers have proposed different approaches for the analysis of reviews, and this study follows the path to propose a sentiment analysis approach for enhanced performance. For this purpose, the calling apps category of the Google Play store was selected.