# Zero-Inflated Patent Data Analysis Using Compound Poisson Models
## 1 Introduction  
Since most of the information is in the form of text documents, the proportion of text data in the big data environment continues to increase [1; 2; 3]. Therefore, many studies for text data analysis have been conducted in various fields [2; 3; 4]. Because the analysis methods of statistics and machine learning cannot analyze text documents as they are, we must preprocess the collected text documents to enable analysis. In the preprocessing of text documents, we use text mining techniques and transform the documents into document-word matrix [5; 6]. The row and column of this matrix represent document and word, respectively. Its elements are frequency values of words occurred in a document. In general, this matrix has a very sparse structure because a word occurring even once among all documents constitutes one column in the matrix [7; 8]. That is, the most values of the elements are zeros because the number of words is much larger than the number of documents [7; 8]. This is the zero-inflated problem [9; 10; 11; 12]. Due to this problem, the document-word matrix has a very sparse data structure. Because of the sparsity, it is difficult to analyze the text document data using convenient methods such as the linear regression model. Research related to zero-inflated text data analysis were carried out in statistics and machine-learning areas [7; 8; 9; 10; 11; 13; 14; 15; 16]. Most of them used the probability distributions such as Poisson or negative binomial [9; 11]. However, most of them had a limitation of model performance because of the sparsity of zero inflation. Therefore, the motivation of this paper is to solve the zero-inflated sparsity problem in the structured text data. First, we survey the theoretical structure of traditional modelsfor zero-inflated data analysis and understand the limitations of the traditional models. Second, we apply the compound Poisson distribution to construct the analytical model for zero-inflated data. In addition, we will show the validity of the proposed method by carrying out performance experiments using real text documents to compare the new proposed and existing analysis models. The objective of our proposed method is to research and develop a predictive model using compound Poisson distribution for an efficient analysis of sparse text data. This is a mixture model of degenerate distribution for zero and continuous distribution for positive numbers greater than or equal to one [17]. The contribution of this paper is to improve the explanatory power of the predictive model by solving the problem of zero excess of text data that occurs in various big data fields. Our research can also expand the contribution of this paper by resolving the zero-inflated problem that occurs in various environments such as the sensor data of the internet of things (IoT), various learning data for artificial intelligence (AI), and medical image video data. For instance, the observed values from the sensing system of the IoT environment are mostly zero values. This is because most of the results of the sensing system of IoT do not change unless a special event occurs. Consequently, we consider an analytical method with compound Poisson distribution for sparse text data analysis. We divide the given data into zero and non-zero parts, and apply the proposed data analysis method suitable for each. Finally, a completed model is constructed by mixing the models applied to each of the two parts. To show the performance of our proposed method, we make experiments using simulated data and practical text data of patent documents. In our experimental results, we use Akaike information criterion (AIC) to evaluate the model performance between the proposed model and traditional models such as linear regression model, generalized linear model and zero-inflated Poisson model. The model with smaller values of AIC has better performance. Furthermore, we show that the AIC value of the proposed method is smaller than other models. Thus, we verify the validity of our research.