## 3 Methodology
### Proposed Model  
We create a model using an auto-encoder and LSTM for each comment. Figure 2 represents the suggested modified LSTM-autoencoder. Here, an auto-encoder is constructed using the LSTM network; hence, we label it as the LSTM-autoencoder network. Comments serve as the input for this LSTM-autoencoder; then, they are encoded, decoded, and reconstructed to their original forms. The network aims to minimize the distance between the reconstructed representation and the original input as it learns. Our initial training of a standard auto-encoder, which is an auto-encoder with only basic neural layers, resulted in very poor performance. The default auto-encoder treats each word as a separate input and does not consider the order or sequence in which they appear in a comment text. Consequently, there was significant loss during the training phase because comment inputs are sequential texts and the LSTM network is specifically designed to handle such sequences of input data. Therefore, we chose the LSTM-autoencoder as the foundational concept for our proposed model. The trustable LSTM-autoencoder network depicted in Figure 2 can be broadly divided into five subparts: (i) a stacked LSTM encoder, (ii) repeated vectors, (iii) a stacked LSTM decoder, (iv) reconstruction loss, and (v) the _WisdomNet_. In the following paragraphs, we describe each of these components. **Stacked LSTM encoder**. The term stacked LSTM network refers to an LSTM network that has multiple stacked LSTM layers. The network becomes more complex and deep as the LSTM layers are stacked. In the stacked LSTM encoder part, a lower LSTM layer provides multiple outputs to its upper LSTM layer, unlike a single output. In other words, it generates an output for each time step of input rather than a single output for all input steps. The last \begin{table}
\begin{tabular}{c c c} \hline \hline
**Text** & **Set** \\ \hline It doesn’t matter if [PERSON] or [PERSON] is extramarrief & NAG \\ Kora. Well said, brother. & \\ \hline What are you looking at? 200 rupees has been said to be 1000 rupees, there & \\ is a mobile phone, \(\overline{\textbf{M}}\)n [PERSON] has come face to face. You read everything & \\ from your reading, \(\overline{\textbf{R}}\)n don’t want to do anything? Here is what we have & \\ said before [PERSON] is doing very wrong. & \\ \hline Kunfu coming from China and kutta coming from Pakistan. & OAG \\ \hline Disliked n unsubscribe. Ohh wait, I wasn’t subscribed. & OAG \\ \hline I hat [PERSON]. & CAG \\ \hline One word for all haters to die somewhere else... & CAG \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Example of fully-noisy machine-translated English dataset with categories. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Set** & **NAG** & **OAG** & **CAG** & **Total** \\ \hline
**English Training** & 4373 & 3096 & 2588 & 10,057 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Label distribution in the fully translated English dataset. layer of the LSTM encoder produces a single output vector that contains details about the entire comment. **Repeat vectors**. The next task is to reconstruct the inputs from the encoded input sequence. This means that we have a single output vector from the stacked LSTM encoder to work with in order to iteratively build the input sequence. We use the repeating vectors to repeatedly apply the output vector to the number of time steps in the output sequence to achieve this result. **Stacked LSTM decoder**. The stacked LSTM decoder then receives these repeating vectors as an input. This indicates that the input to the stacked LSTM decoder was the same at every time step but had a different hidden state. The original input sequence is reconstructed by stacking several LSTM layers. **Reconstruction loss**. The actual output and the expected output for each review \(I_{i}(i_{1},i_{2},\ldots,i_{t})\) are given as \(I_{i}(i_{1},i_{2},\ldots,i_{t})\) and \(O_{i}(o_{1},o_{2},\ldots,o_{t})\), respectively.