# Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models
## 2 Related Work  
Many researchers have been working on classifying app reviews using machine learning and deep learning [4; 5; 6]. As deep-learning-based approaches are considered, [7] presented a method based on deep-learning algorithms for classifying app reviews. The proposed approach was tested on a publicly available dataset. The results show that it outperformed the state-of-the-art methods by achieving a 95.49% precision, 93.93% recall, and 94.71% F1 measure. Another study [8] considered the reviews on the Shopify app and categorized the reviews into happy and unhappy. Several feature extraction techniques were deployed using supervised machine-learning algorithms for sentiment classification. Logistic Regression (LR) proves to be the leading performer with 83% correct predictions. Similar to deep-learning models, several studies have leveraged machine-learning models for sentiment analysis. In the same way, [9] analyzed the performance of the classic BoW model with pre-trained neural networks and concluded that classic models still performed better when integrated with different techniques for textual pre-processing. Similarly, an approach based on supervised machine learning was proposed in [5], which utilized an extra tree classifier (ETC), RF, LR, DT, KNN, etc. to classify accessibility-related reviews. The results showed that ETC performed better than the other models in terms of accuracy. Several studies also considered the domain of software engineering related to mobile apps for research. For example, the classification of non-functional requirements gathered from the app reviews using was performed using CNN and word2 vec vectorization in [10]. The proposed approach achieved an accuracy of 80%. Another similar study is [11], which analyzed the behavior of users regarding software requirements. Thus, the study proposed an approach for the analysis of temporal dynamics of reviews reporting defective requirements. An analysis regarding monitoring app reviews was performed in [12] that first extracted negative reviews, created a time series of the negative reviews, and finally trained the model to identify the leading trends. In addition, this study also focused on automatic classification of the reviews to handle the problem of a large number of daily submitted reviews. In this regard, the study proposed a multi-label active-learning method. The results proved that the proposed methodology of actively querying Oracle for assigning labels gave better performance than the state-of-the-art methods. As manual analysis of a large number of reviews is nearly impossible, topic modeling, a technique that helps in identifying the topics in a given text, has been adopted by many researchers. The study [13] investigated the relationship between the features of Arabic apps and then determined the accuracy of representing the type as well as the genre of Arabic Mobile Apps on the Google Play store. The LDA method was deployed, and important insights were provided that could improve the future of Arabic apps. The authors proposed an NB and XGB approach in [14] to determine the user interaction with the app. In the same way, [15] adopted NB along with the RF and SVM to classify mobile app reviews on the dataset gathered from the Google Play store. [16] also deployed an RF algorithm to determine the factors that differentiate reviews between the US and other countries. The study [17] analyzed shopping apps in Bangladesh. They extracted data from the Google play-store and used VADER and AFFIN for sentiment annotation. Several machine-learning models were used for sentiment classification, and RF outperformed with significant accuracy. The study [18] worked on multi-aspect sentiment analysis using RNN-LSTM. They experimented using Tik-Tok app reviews. They used BERT embedding with the RNN-LSTM model and achieved significant 0.94 accuracy scores. A tremendous amount of research has been performed by researchers on app review sentiment analysis.