## 3 Proposed Model
Since emoji and linguistic data provide information about sentiments, the dataset that contains emoji and linguistic symbols is used as the input to the language model, i.e., pre-training XLNet;
2. Emojis and linguistic symbols are processed in the same way as sentiment words when a pre-trained model is used to model information available on social networks. This leads to the creation of the linguistic symbol word vector as well as the emoticons symbol word vector. This combination produces a multi-dimensional representation of the text's emotions. The following are the primary steps in the multi-dimensional sentiment modeling at the sentence level: 1. The attention network provides prior knowledge of sentimental words. An approach based on word-level attention on the dictionary of sentiment restriction is provided, with the attention coefficients of sentiment-related words being as similar as possible. The attention formula is based on the attention formula at the word level;
2. Vectorized words of language symbols and emoji symbols are given as inputs to a dual-LSTM network integrated with attention; the output is received as the vector of sentences of language symbols;
3. The vectorized words of the emoji symbols are taken as outputs as the vectors of sentences of the emoji symbols directly;
4. Combining the obtained sentence vectors of language symbols with emoticon symbols yields the sentence vectors. The detailed mechanism of sub-modules is discussed below. ### Sentiment Classification Using Multi-Layer Perceptron

The document vector representation is fed into a multi-level perceptron. The following parameter settings shown in Table 1 are used in obtaining optimized performance during sentiment classification. These parameters are obtained by performing several experiments with different parameters. Using the above parameters in Table 1, the multi-layer perceptron (as shown in Figure 2) goes through the learning process and the output class labels are obtained using the below process, the MLP learning Procedure, as shown in Figure 3.

1. Using forward propagation, the data from the input layers are transmitted to the output layer;
2. The error is calculated based on the received output (the difference between the predicted outcome and the achieved outcome); \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline
**Optimization function** & _sgd_ (Stochastic Gradient Descent) \\ \hline
**Batch-Size** & 64 \\ \hline
**Learning rate** & 0.03 \\ \hline
**Number of iterations** & 20 \\ \hline
**Activation Function** & ReLu \\ \hline
**Epochs** & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter settings for the MLP. 3. The error is back-propagated and its derivatives are obtained concerning all weights in the network, then the model is updated. These three steps are repeated over multiple epochs to learn the ideal weights. Finally, the output is achieved through a threshold function to obtain the predicted class labels. The error, i.e., the mean square error, is calculated using the following equation: \[\Delta_{w}(t)=-\in\frac{dE}{dw_{(t)}}+\propto\Delta_{w}(t-1)\] (2)

Figure 3: Learning process of the MLP. Figure 2: The multi-layer perceptron. Here, \(\Delta_{w}(t)\) is the gradient of the current iteration, \(\in\) is the bias, \(dE\) is the error in each iteration, the weight vector is represented by \(dw_{(t)}\), \(\propto\) represents the learning rate, and the gradient of the previous iteration is denoted by \(\Delta_{w}(t-1)\). This process continues until each input-output pair's gradient has converged, which means the freshly computed gradient has not changed more than the set convergence threshold since the previous iteration. Here, the network updates are performed incrementally. ## 4 Results and Discussion

### Data Acquisition

Using the Google Play Scraper package with Python APIs, the dataset for three popular UPI mobile payment apps were collected. The three payment apps were GooglePay, PhonePe, and Paytm.