# AB-LaBSE: Uyghur Sentiment Analysis via the Pre-Training Model with BiLSTM
## 4 Experiments
### Results and Analysis  
In this experiment, we used the \"strict\" standard to evaluate the model's results. In this experiment, P (precise rate), R (recall rate), and F (F1-Measure) value were used as evaluation indexes to evaluate the effectiveness of the experimental method. P refers to the ratio of the number of correctly classified comment texts to the total number of comment texts. R refers to the ratio of the number of correctly classified texts belonging to a certain \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Uyghur** \\ \hline Max sequence length & 64 \\ Max epochs & 10 \\ Batch size & 32 \\ Learning rate & \(2\times 10^{-5}\) \\ Gradient accumulation steps & 1 \\ Warm up proportion & 0.0 \\ Dropout & 0.5 \\ The dimension of the word vector & 768 \\ The number of hidden neurons in the BiLSTM layer & 384 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The hyperparameters of _AB-LaBSE_. emotional orientation to the number of truly emotional orientation comments in the review texts. F value is the harmonic average of precise and recall rate. In this section, by comparing the accuracy and recall rates of these models, we can prove that the _AB-LaBSE_ model used in this paper has the best effect. As can be seen from Tables 3 and 4, the _AB-LaBSE_ model proposed by us has significantly improved in the above three indicators, reaching the optimal value in the comparison model, and its F1 value is also the highest, so the performance of the model is the most stable. By comparing the _mBERT_ model and _Sentence-BERT_ model, the _mBERT_ model does not add Uyghur language data for training, so our five categorical emotion analysis dataset has a poor effect on this model. However, with the increase of data volume, The _mBERT_ model performs much better than the _Sentence-BERT_ model on the binary dataset of hotel reviews. It uses WordPiece embedding to promote the spatial alignment of embedding across languages. The _BERT_ model maps sentences to a vector space. This vector space is not suitable for cosine similarity, however, the _Sentence-BERT_ model overcomes this shortcoming, so in the case of a relatively small dataset, the effect of using the _Sentence-BERT_ pre-training model is better than _mBERT_. By comparing the data in Figures 6 and 7, we can see that the recognition effect of _XLM_ class model is generally better than that of the _BERT_ class model. A large part of the reason is that the _XLM_ class model now uses a large shared statement block model to mark strings and adds the Uyghur language dataset to the model. _XLM-align_ also introduces denoising Word alignment before cross-language training, which is better than the above two models. It can also be seen from the experimental results that the F1 of _XLM-R_ is higher than that of _XLM-align_. In the two-category experiment, the F1 value of negative increased by 15.6%, and the F1 value of positive increased by 2.02%. This is because _XLM-R_ combines _XLM_'s cross-language and Roberta's improved optimization function and larger model parameter number, so as to improve the accuracy of recognition. Its language processing for low-resource languages is also very good. We can see that the pre-training model has achieved the best results in the downstream tasks, and the F1 value has improved a lot. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **P(\%)** & **R(\%)** & **F1(\%)** \\ \hline _mBERT_ & 48.14 & 48.13 & 46.57 \\ _Sentence-BERT_ & 51.47 & 53.12 & 51.04 \\ _XLM-align_ & 61.13 & 58.8 & 58.34 \\ _XLM-R_ & 62.08 & 56.93 & 58.00 \\ _AB-LaBSE_ & **76.78** & **77.19** & **76.04** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of different algorithms in five categorical datasets of emotion analysis.