# 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  
The LDA topic extension features were obtained according to the fusion method mentioned in 1.2.2 above. Spliced with the word vector, they were input to the BiLSTM in this paper. The context related information was extracted using the BiLSTM. The weights were assigned by adding the self-attention mechanism within each channel, and finally input to the softmax classifier, and thus better classification results were obtained. To verify the classification effect of the model in this paper, the LBSA model was compared with other single models and combined models. The models compared with it were CNN, LSTM, GRU, BiLSTM, CNN-LSTM, GRU-CNN, LDA-CNN, LDA-BiLSTM, CNN-Self-Attention, and BiLSTM-Self-Attention. The CNN in the experiment adopted a 3-layer model architecture with word vector convolution windows set to 3, 4, and 5 respectively. The LSTM time step was set to 20, the vector dimension was 100, the number of cells was 128, the dropout was 0.5, the batch sample size was 32, the number of training rounds was 30, and the optimizer was Adam. The GRU the vector dimension was 100, the dropout was 0.5, the batch sample size was 32, the number of training rounds was 30. Due to the small data set, K-fold cross validation [37] was applied in the experiment. The whole data set is divided into k equal subsets, and the algorithm is repeated k times. One subset is randomly selected each time \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Topic** & **Top 10 Probability Words** & **Tags** & **Number of Documents** & **Percentage (\%)** \\ \hline Topic 0 & Buy, order, hope, delicious, beat, delivery, & Buy & 1358 & 16.4 \\ Topic 1 & Money, Icatty, gram, pot, block, 100, net & Weight & 1492 & 18.1 \\ Topic 2 & Delicious, buy, good, very, nice, really, repo, & Delicious & 1309 & 15.8 \\ Topic 3 & Eat, pecan, buy, like, original taste, fragrant, & Taste, too sweet, want, salt and pepper & Taste & 1098 & 13.3 \\ Topic 4 & Peel, eat, no, desicent, like, no, shell, jar, & Hand Peeled & 900 & 10.9 \\ Topic 5 & Walnut, pecan, buy, this year, eat, small, & Quality & 945 & 11.4 \\ Topic 6 & Price increase, expensive, buy, 99, too & Price & 11.41 & 13.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Carya cathayensis review word-topic distribution and classification results. as the test set, and the remaining sample set is used for training [38]. In this paper, k = 10 is selected. The average value of the results of multiple 10-fold cross-validation is taken as the final result of the model. After several experiments, the comparison results of single model are shown in Table 4, and comparison results of the combined models are shown in Table 5. The comparison of the accuracy rate of cross-validation of the combined model with ten folds is shown in Figure 6.  
From the experimental results in Table 4, it can be seen that the single model achieved excellent results in the evaluation index results. Analysis of the experimental results led to the following conclusions:  
1. Comparing the CNN with the LSTM, the performance of the CNN is significantly better than that of the LSTM. The reason for this is that the dataset crawled in this paper contains short texts targeted at the sales, quality, and price of videos about _Carya cathayensis_, which is not firmly contextualized and has sparse text features. The CNN is good at capturing local features in text, while LSTM is good at capturing contextual features, so the precision of the CNN was 1.23% higher than that of the LSTM. 2. Comparing the LSTM with the GRU, the accuracy of GRU is about 1.54% lower than that of LSTM. GRU is as effective as LSTM in capturing semantic associations between long sequences, but its structure and calculation are simpler than LSTM, and it may not be as effective as LSTM in processing small amounts of short text.