## 3 Proposed Model
Google Play Scraper offers Python APIs for crawling the Google Play Store without external dependencies. The details of the dataset obtained are as shown in Table 2. Here, we considered only positive and negative reviews, while neutral reviews were not considered. In this process, the equations are numbered consecutively, with equation numbers shown in parentheses flush with the right margin of the column, as in (1). First, use the equation editor to create the equation. Then, select the \"Equation\" markup style. Press the tab key and write the equation number in parentheses. To make your equations more compact, you may use the solidus (/),exp function, or appropriate exponents. Use parentheses to avoid ambiguities in denominators. Punctuate equations when they are part of a sentence, as in:

\[B_{p}+H_{2}=40. \tag{3}\]

### Data Augmentation

A balanced dataset facilitates the establishment of unambiguous decision limits for every class and enables models for the classification of data more precisely in any classification task. Any unbalanced dataset can be converted to a balanced one using data augmentation techniques, guaranteeing that the dataset is consistent across labels. The algorithm is named SMOTE [51], and is a commonly used data augmentation approach that may be used for any dataset without any influence on predictions based on a particular label. SMOTE samples the class with a minority with the help of a k-nearest neighbours classifier; it selects samples close to the feature space and generates synthesized data points. In this study, we use SMOTE to balance the dataset in terms of the labels and performs an evaluation. ### Performance Measurement

To assess how well the suggested model works, an accuracy matrix is computed. For positive sentiment classification, true positive and false positive variables are identified. For negative sentiment classification, the true negative and true positive variables are defined as shown in Table 3. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Total Reviews** & **Positive** & **Negative** \\ \hline GooglePay & 45,597 & 20,975 & 24,622 \\ \hline PhonePe & 43,209 & 17,715 & 25,494 \\ \hline PayTM & 47,932 & 33,073 & 14,859 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Datasets. Using the parameters in Table 3, the following equation is defined to assess the accuracy of the proposed model:

\[\text{Accuracy}(Z)=\frac{X_{1}+X_{2}}{Y_{1}+Y_{2}+X_{1}+X_{2}} \tag{4}\]

### Performance Evaluation

For a clear view of and simplicity in the graphical representations, the models are termed hereafter as shown in Table 4.

A hyperparameter is a value for a parameter that is used to influence the learning process. Different hyperparameters are tuned for optimized performance accuracy. Comprehensive experiments are performed using several hyperparameters, such as the embedding type, activation function, and dropout. The deep learning methods CNN and BiLSTM with different word embedding methods, i.e., Word2 Vec and BERT, are tested on different hyperparameters. The proposed model is also tuned with several hyperparameters. The hyperparameter tuning process is performed with different embedding combinations on 200, 300, and 400 words and with learning rates ranging from 0.01 to 0.10. The observations of these experiments are shown in Tables 5 and 6. The above Table 5 provides the performance accuracy rates of different models with an embedding size of 200 with dropout from 0.01 to 0.10. All models M01, M02, M03, M04, M05, M06, M07, M08, and M09 are tested using this combination. It can be observed that the proposed model achieves the highest classification accuracy rate of 96.62% using a dropout rate of 0.10 for dataset 1. For dataset 2, the highest accuracy can be observed for the dropout of 0.04 with 95.95% accuracy. At the same time, 96.36% accuracy is obtained for dataset 3 at a dropout rate of 0.04. The accuracy rates of the other models vary depending on the different dropout values.