# A Small-Sample Text Classification Model Based on Pseudo-Label Fusion Clustering Algorithm
## 1 Introduction  
Text classification is the process by which natural language processing techniques accurately and rationally correspond texts to corresponding categories based on the classification system or criteria developed by users' needs and is widely used to analyze people's emotions and attitudes toward products and services [1]. In recent years, deep learning-based training models have dominated the research on text classification methods. These models, such as Word2 Vec [2], BERT [3], and ELECTRA [4], are first pre-trained on a large scale and then fine-tuned to transfer generic knowledge to specialized sentiment tasks. Such training methods can effectively meet the needs of sentiment classification tasks. However, the accuracy of the sentiment classification results produced by these models is limited when there is not enough labeled training data for practical production. Although a large amount of unlabeled text data can be easily collected in living scenarios, the labeling itself still requires quite a lot of cost, and there are problems such as uncontrollable labeling time, high cost of manual labeling, and uneven quality of labeling [5]. Such problems are more obvious in some niche and specialized fields, including but not limited to the legal field, medical field, and financial audit field. Therefore, those classification methods that require only a limited number of labeled data are receiving more and more attention. Researchers begin by looking at the model and the data itself. [PERSON] et al. [6] proposed universal language model fine-tuning (ULMFit) to fine-tune the language model by varying the learning rate in both longitudinal and cross-sectional dimensions, allowing the model to converge faster on small-sample datasets. However, limited by the differences between the source and target datasets, there are problems that can easily lead to overfitting of the prediction results. [PERSON] et al. [7] experimented with variouspre-processing techniques, including semantic lexical complementation, synonym replacement, and random exchange, from the perspective of improving the diversity of labeled data, all of which effectively improved the accuracy of the classifier. [PERSON] et al. [8], on the other hand, used various data augmentation means such as grammar tree transformation, back translation, random insertion, and random deletion to perform sentiment analysis on text. Some researchers use language models or agent networks for data augmentation. For example, the LAMBDA (language-model-based data augmentation) [9] technology proposed by the IBM team uses GPT-2, which has been pre-tuned on the training set, to generate labeled augmented sentences, and then filters the augmented sentences through the classifier to ensure data quality. Due to the confidence checking of the classifier, the sample data obtained by this method are better than other data enhancement methods, but the cost of over-emphasizing data confidence is at the expense of efficiency and data diversity. The data augmentation framework EPiDA (Easy Plug-in Data Augmentation) proposed by [PERSON] et al. [10] improves this point. Based on commonly used data augmentation methods, EPiDA uses classifier feedback to achieve relative entropy maximization (REM) and conditional entropy minimization (CEM). The diversity and quality of the enhanced samples are balanced by the sum of REM and CEM, and better-enhanced samples are selected. Compared with the LAMBADA method, the basis of EPiDA is the commonly used data enhancement method, which has high enhancement efficiency, strong applicability, and a more reasonable method for screening enhanced samples. This approach is easy to understand, effective, and has inspired us a lot. However, the data augmentation methods are mostly oriented to labeled data, which neither utilize the much larger amount of unlabeled data, which is prone to information loss nor substantially increase the feature information of the text, which is vulnerable to the small sample size.