# Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM
## 4 Experiments and Results
### Experimental Results
#### 4.6.1 Comparison of Model Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **F1/\%** \\ \hline CNN [15] & 87.66 & 86.61 & 87.13 \\ LSTM [16] & 86.43 & 85.12 & 85.77 \\ GRU [17] & 84.89 & 83.02 & 83.94 \\ BiLSTM [18] & 87.39 & 87.02 & 87.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Single model comparison results. Figure 6: Comparison of Accuracy in 10-fold Cross-validation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **F1/\%** \\ \hline CNN-LSTM [19] & 87.61 & 86.23 & 86.91 \\ GRU-CNN [20] & 86.12 & 84.71 & 85.41 \\ LDA-CNN & 88.07 & 87.48 & 87.77 \\ LDA-BiLSTM & 88.92 & 86.36 & 87.62 \\ CNN-Self-Attention & 88.73 & 86.90 & 87.80 \\ BiLSTM-Self-Attention & 89.49 & 88.42 & 88.95 \\ LBSA & 91.65 & 90.17 & 90.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Combined model comparison results. 3. Comparing the BiLSTM with the CNN and the LSTM, the performance of the CNN is not much different from that of the BiLSTM. The BiLSTM doubled the computational complexity of the model by stitching the output of the LSTM in both directions at the same time, which improved the precision of the BiLSTM by approximately 0.96% compared with the LSTM. From the experimental results in Table 5 and Figure 6, the accuracy of the combined model fluctuates but tends to remains fixed after 10-fold cross-validation. It can be seen that the feature fusion-based LBSA model proposed in this paper achieved excellent results in the evaluation index results with a precision rate of 91.65%, and the precision and recall rate improved by 2-5% compared with other models. Compared with other models, the following conclusions can be drawn:  
1. Comparing the CNN-LSTM model and GRU-CNN model mentioned in the literature review with the LBSA model in this paper, the accuracy of LBSA is significantly higher than the two models. CNN-LSTM model and GRU-CNN model have good classification effect in other papers, but the classification effect in this paper is not so good. The reason may be that there are big differences between different data sets and different experimental parameters. 2. Comparing the LDA-CNN model with the LDA-BiLSTM model, the accuracy of the LDA-BiLSTM model is 0.85% higher than that of the LDA-CNN model. The accuracy of the feature fusion model is higher than that of the single model, which indicates that feature fusion has great influence on short text classification. 3. Comparing the CNN-Self-Attention model with the BiLSTM-Self-Attention model, the introduction of the Self-Attention mechanism on the basis of the CNN and the BiLSTM led to a significant improvement in the classification effect of the model. The precision was improved by 1.07% and 2.1% compared with the two models. The attention mechanism gave different weights to different text features, reducing the influence of noise on the classification, and helped to focus on the key features that affect the classification effect of short text, improving the classification model precision. The BiLSTM-Self-Attention model was slightly more effective than the CNN-Self-Attention model, so it was chosen to make further improvements.