# Research on Online Review Information Classification Based on Multimodal Deep Learning
## 4 Introduction to Multimodal Learning and Modeling
BiGRU, on the other hand, introduces more state information through the inverse layer, which improves the handling of long sequences and reduces the risk of vanishing or exploding gradients. In combining BiGRU and Softmax, the input text sequence can be encoded using BiGRU, and then the encoded result is passed to a fully connected layer, and finally classified using the Softmax activation function. Specifically, the output of BiGRU can be used as an input to the fully connected layer, and the output of the fully connected layer is then used for label prediction via Softmax. This combination can effectively improve the performance of text categorization, especially when facing complex text datasets. The BiGRU model operates on a given sequence of input vectors \(<x1,\ x2,\ \dots,\ xt>\) (where \(xt\) denotes a concatenation of input features) and computes the corresponding hidden activations \(<h1,\ h2,\ \dots,\ ht>\). At the same time, a sequence of output vectors is generated from the input data \(<y1,\ y2,\ \dots,\ yt>.\) At time \(t\), the current hidden state is determined by three components: the input vectors \(<x1,\ x2,\ \dots,\ xt>\), the forward hidden state, and the backward hidden state. The reset gate \((r_{t})\) controls how much previous state information is ignored; the smaller the value of \(r_{t}\), the more previous state information is ignored. The update gate \((z_{t})\) controls the extent to which the unit state receives new input information. The symbol \(\otimes\) represents elemental multiplication, \(\sigma\) denotes a sigmoid function, and tanh denotes a hyperbolic tangent function. The hidden state \(h_{t}\), update gate \((z_{t})\), and reset gate \((r_{t})\) of the BiGRU are computed by Equations (6)-(9). \[Z_{t}=(w_{z}[h_{t-1},x_{t}]) \tag{6}\]  
\[r_{t}=(w_{r}[h_{t-1},x_{t}]) \tag{7}\]  
\[h_{t}=GRU(x_{t},h_{t-1}) \tag{8}\]  
\[h_{t}=w_{t}h_{t}+v_{t}h_{t}+b_{t} \tag{9}\]  
Softmax functions are widely used in tasks such as text categorization, sentiment analysis, and machine translation. Often, we need to represent a piece of text as a vector to facilitate subsequent computation and analysis. One of the common ways to represent text vectors is to use the word embedding technique to map each word to a low-dimensional vector of real numbers, and then transform the entire text into a fixed-length vector through some aggregation or transformation operations. In the following algorithm, \(w_{a}\) represents the weight matrix of the attention function, \(tanh\) refers to the hyperbolic tangent function, \(y\) represents the sentiment analysis results, and \(b_{a}\) represents the corresponding bias of the output layer. \[u_{t}=tanh(w_{a}h_{t}+b_{a}) \tag{10}\]  
\[\alpha_{t}=\frac{\exp\left(u_{t}^{T}u_{a}\right)}{\sum_{t}\exp\left(u_{t}^{T} u_{a}\right)} \tag{11}\]  
\[s_{it}=\sum_{i=1}^{n}a_{it}h_{it} \tag{12}\]  
\[y=softmax(w_{a}s_{it}+b_{a}) \tag{13}\]  
After completing the text vector representation, we also need to perform tasks such as classification and labeling, see Figure 5 below. At this point, the Softmax function can be used to map the text vectors to different classes of probability distributions (the processes represented by the arrows pointing to them in the figure below). Specifically, in natural language processing tasks, a neural network model is usually used as a classifier, with text vectors as inputs, and after several layers of fully connected layers and nonlinear activation functions, the outputs are finally mapped to individual categories using the Softmax function, and the probability values of each category are calculated. Ultimately, we can consider the category with the largest probability value as the category to which the text belongs. This paper investigates the Bert BiGRU Softmax model for sentiment analysis of online product quality reviews. The sentiment Bert model is used as an input layer for feature extraction in the preprocessing stage.