# Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models
## 3 Methods and Proposed Approach  
This study aims to classify the calling app reviews using machine-learning and deep-learning algorithms and NLP techniques. We perform aspect-based sentiment analysis on calling app reviews to determine which app is better for different features. Classification and topic modeling algorithms are used to find significant topics and classification results. Finally, we discuss the significance of apps according to their features and recommend improvements to app companies regarding the features with negative user sentiments.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref** & **Year** & **Model** & **Aim** & **Dataset** \\ \hline
[16] & 2019 & RF & Analyzing User Reviews & Apple play store \\ \hline
[15] & 2019 & NB, Multinomial Naive Bayes, RF, and SVM & Mobile-App Reviews & Google Play Store (90,000 reviews collect from 10 apps) \\ \hline
[12] & 2019 & DT, KNN, MLP, and RF & Classifying user reviews & Google play store, Apple play store \\ \hline
[7] & 2020 & CNN & textual/non-textual information of apps & Google play store \\ \hline
[14] & 2020 & NB, XGBoost, Deep Learning, MLP & User Interaction within Apps & Google Play Store \\ \hline
[9] & 2020 & KNN, MNB, SVM, and MLP & App Reviews Classification & Google Play Store \\ \hline
[8] & 2020 & RF, AdaBoost Classifier, LR & Classify the reviews & Google play store \\ \hline
[10] & 2021 & CNN & Classify app reviews & Google play store \\ \hline
[19] & 2021 & NB, SVM, D,T and RF & Automatic Classification & Heedzy.com \\ \hline
[13] & 2021 & LDA & Arabic Apps Classification & Google Play Store \\ \hline
[20] & 2021 & SMLT & Classify app reviews & Google play store \\ \hline
[21] & 2021 & LR, K-NN, Stochastic Gradient Descent, DT, RF, and SVM & Classifications on the App’s success & Google Play Store \\ \hline
[5] & 2022 & ETC, RF, SVC, DT, KNN, and LR & App Reviews Classification & Android Apps \\ \hline
[11] & 2022 & K-Means, Re-bert & Opinion Mining & App store \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of the related work.  
We also present an overall comparison between calling apps and suggest to users where they can find better services for their needs. The diagram of the proposed approach is shown in Figure 1. The first step is to collect the data for mobile apps. The data for five calling apps are collected to perform analysis. The data are collected from the Google Play Store using a web scraper. After extracting the dataset, aspect extraction and sentiment extraction are performed to label the dataset.  
We proposed a supervised machine-learning approach for sentiment classification for which we required a labeled dataset; however, the original dataset was not labeled. We used VADER for data labeling with three sentiments negative, positive, and neutral. After data labeling, we used learning models. The LDA was used for aspect extraction, while VADER was used for sentiment extraction. The aspects and sentiments were used to label the dataset. The labeled dataset was further preprocessed to remove noise and unnecessary data.  
In preprocessing, punctuation removal, numeric value removal, case conversion, stemming, and stopwords removal were performed. Preprocessing made the dataset clean and ready for split into training and testing sets with 0.8 to 0.2 ratios, where % of the dataset was used for the training of models and 20% of the dataset was used for the evaluation of models. The training and testing sample ratio is shown in Table 3.  
The training and testing sets were used for feature extraction techniques using TF-IDF, BoW, and Hashing Vectors. Machine-learning models were trained on these features, while deep-learning models do not require feature extraction techniques. Both machine-learning and deep-learning models were evaluated in terms of the accuracy, precision, recall, F1 score, and G-mean.