# An Analysis Method for Interpretability of CNN Text Classification Model
## 1 Introduction  
Text classification refers to automatically classify and mark text sets using a computer according to a certain classification system or standard. It finds the model of relationship between document features and document categories based on a set of marked training documents, and then uses the learned relationship model to make category judgments on new documents. With continuous development of artificial intelligence, text classification has gradually changed from a knowledge-based method to a method based on statistics and machine learning. At present, two basic deep learning algorithms for processing sequences are recurrent the neural network and one dimensional convolutional neural network [1]. Among them, is a very important and efficient way to classify text based on the convolutional neural network (CNN) model, because the CNN text classification model can achieve better prediction accuracy and consume fewer computing resources [2]. [PERSON] trained the CNN on the pre-trained word vectors for experiments of sentence level classification task and proved that a simple CNN can obtain good results on multiple benchmarks with only a few super parameter adjustments and static vectors [3]. [PERSON], H proposed a kind of convolutional neural network for the task of emotion classification and proved the effectiveness of using a continuous convolutional layer forlonger texts through experiments with three famous data sets. Therefore, CNN-based text classification has gradually become a research hotspot when dealing with issues of text classification. The current research on CNN-based text classification can be roughly divided into the following two categories. One is research on the method of CNN-based text classification [4]. [PERSON] et al. proposed a framework based on high utility neural networks for text classification. Which can effectively mine the importance of the text features and their association. MHUI (Mining High Utility Itemsets) from databases is an emerging topic in data mining. It can mine the importance and the co-occurrence frequency of each feature in the dataset. The co-occurrence frequency of the feature reflects the association between the text features. Using MHUI as the mining layer of HUNN (High Utility Neaural Networks), it is used to mine strong importance and association text features in each type, select these text features as input to the neural networks. Then, acquire the high-level features with a strong ability of categorical representation through the convolution layer for improving the accuracy of model classification [5]. [PERSON] et al. proposed that the effectiveness of such techniques has not been assessed for the hierarchical text classification (HTC) yet. This study investigates the application of those models and algorithms on this specific problem by means of experimentation and analysis. They trained classification models with prominent machine learning algorithm implementations--fastText, XGBoost, SVM (Support Vector Machines), and Keras' CNN--and noticeable word embeddings generation methods--GloVe, word2 vec, and fastText--with publicly available data and evaluated them with measures specifically appropriate for the hierarchical context. FastText achieved an LCAF1 (Least Common Ancestors) of 0.893 on a single-labeled version of the RCV1(Ruters database) dataset [6]. [PERSON], [PERSON] proposed a new architecture of CNN based on multiple representations for text classification, by constructing multiple planes so that more information can be dumped into the networks, such as different parts of text obtained through a named entity recognizer or part-of-speech tagging tools, different levels of text embedding, or contextual sentences. Various large-scale, domain-specific datasets are used to validate the proposed architecture. It can obtain further gains in performance over state-of-the-art deep neural network models [7]. [PERSON], [PERSON] et al.