## 3 Methodology
### Deep Learning Algorithms  
We proposed three DL models, deep convolutional neural networks (DeepCNN), hybrid CNN-LSTM based on CNN and LSTM, and hybrid CNN-GRU based on CNN and GRU. The architectures of the three models are shown in Figure 2. Each model is described in detail as follows. * The DeepCNN model includes different layers: the embedding layer, three CNN layers, two MaxPooling layers, Global MaxPooling, flatten, fully connected, and output layers. * The first layer utilized the embedding layer as a pre-processing step to turn the vector representation into a fixed-sized denser vector representation [51]. It is implemented in the Keras library [52]. Input-dim, output-dim, and input-length are the three parameters that are employed. Input-dim provides the vocabulary size of the dataset, output-dim describes the vector space in which words will be embedded, and input-length describes the length of input sequences. Because both the CBOW and the skip-gram Model are 300 d vectors long, we set the output-dim to 300, the input-dim to 20,000, and the input length to 140. * The multi-layer neural network known as convolutional neural networks (CNN) is an enhancement of the error backpropagation network [53]. It has a feature map and a convolution filter (kernel). The convolution filter is applied to the input word matrix to create a feature map identifying significant input data patterns. The base of the convolutional operation is the kernel function. Feature extraction is completed by sliding the kernel from top to bottom and from left to right in the input matrix. Each filter also makes use of the rectified linear unit (ReLU) activation function [54] to recognize various aspects of the news. * The max-pooling layer down samples the feature maps to be more resilient to the probable changes of a feature's position in the text by recapping the feature's presence in patches of the feature map. Calculate the maximum value for each feature map patch. The two-dimensional arrays of the combined feature maps are flattened into a single, lengthy continuous linear vector using the flatten layer [51]. * The fully connected layer is a dense and deeply connected layer compared to its preceding layer and changes the dimension of the output by implementing a vector-matrix multiplication. * The output layer uses the output of the fully linked layer to determine if the tweets are positive or negative. In this layer, the ADAM optimizer [55] was employed, and the activation function is sigmoid [56]. * The embedding layer, CNN and MaxPooling layers, long short-term memory (LSTM), fully linked layer, and output layer are all components that constitute the hybrid CNN-LSTM model. LSTM is a recurrent neural network dependent on DL technology. LSTM is an improved version of RNN that differs from it in one important way: its architecture incorporates a memory cell at the top that allows for efficient information transmission from one instance to the next. In comparison to RNN, it can recall a large amount of information from previous states while avoiding the vanishing gradient problem [57]. By using a novel additive gradient structure that gives direct access to forgotten gate activations, LSTMs are able to tackle the vanishing gradient issue. By often changing the gates at each stage of the learning process, the network may exploit the error gradient to encourage desired behavior. A valve is used to add information to or remove it from the memory cell. The LSTM receives an input from the hidden layer of the current time instance and output from the hidden layer of the prior time instance. These two pieces of data pass via a number of network activation functions and valves before exiting at the output. The LSTM has three gates: an input gate, an output gate, and a forget gate. The forget gate and the input gate, as depicted in Figure 3, select information to be cleaned and appended to the cell state. The cell state can be updated after these two points are known. Finally, the output gate determines the network's final output [57].