## 3 Proposed Research Methodology
### Modeling Methods  
CNN is a deep neural network that maneuvers the computational complexity of large-sized data [26]. CNN is an efficient neural network model and learns complex features with the help of convolution, nonlinear activation, and dropout and pooling layers [27]. It was designed for image-related tasks, such as image segmentation and image classification [28].  
In CNN, training is performed in an end-to-end fashion, which makes it more efficient. To encode semantic information, fully connected layers are utilized at the end of the model. It is a feed-forward network where filters are applied to the output of the previous layer to map features. The main components of the CNN model are convolutional layers, pooling or sub-sampling layers, a flatten layer, an activation function, dropout, and a fully connected layer. Features are extracted by convolutional layers, and then the output of the convolutional layers is fed to the fully connected layers. The pooling layer reduces the features mapped by convolutional layers to reduce overfitting probability. Pooling can be a max or average layer, where the max-pooling layer chooses sharp features as compared to the average pooling layer. The flatten layer converts the data into an array so that it can be fed to the fully connected layer. This study utilizes the rectified linear unit (ReLU) as an activation function:  
\[y=max(0,i), \tag{1}\]  
where \(y\) represents the activation output and \(i\) represents the input. Convolution layers extract local and high-level features by assigning weights to the kernel during the training phase. CNN has been widely used in disease diagnosis. In binary classification, the cross-entropy error is used as a loss function; this has also been used in this study. It is computed as:  
\[cross-entropy=-(i*log(p)+(1-i)log(2-p)), \tag{2}\]  
where \(i\) represents the indicator of class labels, a log is a natural log, and \(p\) represents the probability that is predicted.  
As CNN is a modification of the backpropagation algorithm; therefore, sigmoid is utilized as the error function for output. The CNN model generates output as three neurons for each case of the target class. CNN has been regarded as a robust model for classification tasks in the medical field. CNN has been utilized by many researchers for various classification tasks, such as lung-disease classification [29], the segmentation of brain tumors [30], and X-rays of the chest [31]. In previous literature, CNN has also been analyzed for text categorization, such as text sentiment analysis [32], text summarization [33], and text report classification [34]. CNN has been employed to detect vision-threatening eye diseases using medical reports in [35].  
Figure 1 presents the proposed framework for the sentiment analysis of in-text citations. The CNN model used in this study has been optimized through a customized structure in terms of the number of layers, number of neurons, and optimizer choice, etc. Details for the CNN architecture are provided in Table 2. In addition to CNN, several well-known machine learning models have been used for the task at hand, including RF, SGD, LR, and LSTM for comparison. The performance of these models is optimized by fine-tuning different hyperparameters, a list of which is given in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Structure** \\ \hline CNN & Conv (7 \(\times\) 7, @64), Max pooling (2 \(\times\) 2), Conv (7 \(\times\) 7, @64), GlobalMax pooling (2 \(\times\) 2), Dropout (0.5), Dense (32 neurons), Softmax (3), Categorical cross entropy \\ \hline LSTM & LSTM (100 neurons), Dropout (0.5), Dense (32 neurons), Softmax (3) \\ \hline RF & n\_estimator = 200, max\_depth = 30, random\_state = 52 \\ \hline SGD & penality = “12”, loss = “log” \\ \hline LR & penalty = “12”, solver = “, lbfgs” \\ \hline VC & Voting = “soft” \\ \hline \hline \end{tabular}
\end{table}
Table 2: The layer structure and hyperparameters of the learning models.