# AB-LaBSE: Uyghur Sentiment Analysis via the Pre-Training Model with BiLSTM
## 2 Related Works  
Sentiment analysis has a wide range of applications, which are an important task in NLP. We summarized the related works on three topics: (1) data augmentation; (2) cross-lingual pre-trained language models; and (3) _BiLSTM_. **Data augmentation**. Data augmentation is a class of methods used for synthesizing new data from existing data. It has played an important role in the experiment. We find that the larger the scale and the higher the quality, the better the generalization ability of the model. Data augmentation solves the problem of data accuracy by using original documents to generate similar examples, solving the problem of insufficient data effectively. It is an effective method used to expand the sizes of the data samples. In recent years, more and more people are creating data augmentation techniques in research. According to the diversity of generated samples, methods of data augmentation are divided into the following three categories: (1) Paraphrasing; (2) Noising; (3) Sampling. Paraphrasing is used to make some changes to the words, phrases, and sentence structure in the sentence, keeping the original meaning. This method can make use of dictionaries, knowledge maps, semantic vectors, _BERT_[11] models, rules, Machine Translation, etc. It can randomly replace non-stop words with synonyms. For example, back translation [18] can change the syntactic structure and retain semantic information, and it can often increase the diversity of textual data. However, the data generated by the back-translation method depends on the quality of the translation, and most of the translation results are not very accurate. Noising is used to increase some discrete or continuous noises while keeping the label unchanged, which has little impact on semantics. People are immune to noise when reading text, such as words out of order and typos [19]. Based on this idea, some noise can be added to the data to improve the robustness of the model. Since there are few synonyms in Uyghur language, replacing synonyms does not produce positive meaning. The method of data augmentation with noise is simple to use, but it will affect the sentence structure and semantics, with limited diversity, and mainly improve the robustness [20]. Sampling aims to select new samples based on the current data distribution, which will generate more diverse data. Sampling aims to select new samples based on the current data distribution, which will generate more diverse data [21]. Sampling refers to sampling new samples from the data distribution. Different from general paraphrasing, sampling is more task-dependent and needs to increase more diversity while ensuring data reliability. Rules, Seq2 Seq Models, Language Models, and Self-training are usually used in experiments to increase data diversity. Above all, the method of data augmentation can be used as a powerful tool to solve the problems of data imbalance and missing data quickly when we train the NLP model. **Research on sentiment analysis in machine learning. ** Firstly, we will analyze the research of sentiment analysis function in machine learning. [PERSON] [22] proposed the use of a convolutional neural network, CNN, to complete sentiment analysis and problem classification, which achieved good results. [PERSON] [23] proposed to use undersampling and dynamically generating random subspace strategies to solve the problem of unbalanced datasets in sentiment analysis. After the above analysis, we can conclude that it proved these methods to be simple and effective in sentiment analysis task, but there are some limitations: Without a large amount of manual annotation data, traditional machine learning methods will not perform well. The method, based on a shallow neural network, relies on grammatical features and word order features and has difficulties grasping the deep semantic information. We have paid little attention to the low-resource Uyghur language.