# Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM
## 3 Models and Improvements
### BiLSTM  
The traditional recurrent neural network (RNN) has the problem of long-time dependence of sequence data when processing a large amount of data, that is, the problem of gradient dispersion when processing nodes with relatively distant time series. The memory cells in the LSTM [30] model can store the information of data with relatively long time series and transmit information through the gate structure with only a small amount of linear interactions. It reduces the loss of data, enabling the storage cells to effectively store the semantic information of longer sequences, which effectively solves such problems mentioned above. There are three gate structures in the LSTM: input gates \(i_{t}\), forget gates \(f_{t}\), and output gates \(O_{t}\). At moment \(t\), the input to the LSTM is \(x_{t},\ h_{t-1}\), and the output is \(h_{t},\ y_{t}\). First, read the input at moment \(t\) and use the sigmoid function to calculate the information lost by the forget gate cells:  
\[f_{t}=\sigma\ \Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{8}\]  
The input gate is used to determine which information to update in the cell. First, calculate the candidate information of the cell:  
\[\widetilde{M_{t}}=\tan\ h\ (W_{m}\cdot[h_{t-1},x_{t}]+b_{m}) \tag{9}\]Calculate what information to use for cell update:  
\[i_{t}=\sigma\;(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{10}\]  
Use \(f_{t}\) multiplied by the old cell state \(M_{t-1}\) plus the new cell candidate information to complete the update of the cell information:  
\[M_{t}=f_{t}\times M_{t-1}+i_{t}\times\widetilde{M_{t}} \tag{11}\]  
Use the output gate to determine the cell state information to be output:  
\[O_{t}=\sigma\;(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{12}\]  
Finally, use the tanh function to calculate the output cell information:  
\[h_{t}=O_{t}\times\tan hM_{t} \tag{13}\]  
where \(b_{f}\), \(b_{m}\), \(b_{i}\), \(b_{o}\), etc. represent the bias function, \(W_{f}\), \(W_{m}\), \(W_{i}\), \(W_{o}\), etc. represent weights, and \(\sigma\) represents the sigmoid function. BiLSTM concatenates the forward and backward outputs of the LSTM at moment t:  
\[h_{t}=\overset{\rightarrow}{[h_{t};h]}\;\in R \tag{14}\]  
In this paper, the BiLSTM takes the feature fusion vector of the previous layer as input and assigns random initialization to the weight \(W\) and bias function \(b\). The LSTM unit calculates the values of forget gate \(f_{t}\), input gate it, and output gate \(o_{t}\) based on the hidden layer information \(h_{t}-1\) of the previous moment and the input information \(x_{t}\) of the current moment. It then integrates them with the storage unit information \(c_{t}-1\) of the previous moment to obtain the unit output \(h_{t}\) of the current moment while updating the hidden layer information and the storage unit information as input to the LSTM unit of the next moment. Finally, the output vectors of the LSTM units of the previous and next moments are spliced as the output of the BiLSTM [31] with bi-directional semantic features.  
Figure 2: Flow chart of topic feature fusion.