Zero-Inflated Text Data Analysis using Generative Adversarial Networks and Statistical Modeling

In big data analysis, various zero-inflated problems are occurring. In particular, the problem of inflated zeros has a great influence on text big data analysis. In general, the preprocessed data from text documents are a matrix consisting of the documents and terms for row and column, respectively. Each element of this matrix is an occurred frequency of term in a document. Most elements of the matrix are zeros, because the number of columns is much larger than the rows. This problem is a cause of decreasing model performance in text data analysis. To overcome this problem, we propose a method of zero-inflated text data analysis using generative adversarial networks (GAN) and statistical modeling. In this paper, we solve the zero-inflated problem using synthetic data generated from the original data with zero inflation. The main finding of our study is how to change zero values to the very small numeric values with random noise through the GAN. The generator and discriminator of the GAN learned the zero-inflated text data together and built a model that generates synthetic data that can replace the zero-inflated data. We conducted experiments and showed the results, using real and simulation data sets to verify the improved performance of our proposed method. In our experiments, we used five quantitative measures, prediction sum of squares, R-squared, log-likelihood, Akaike information criterion and Bayesian information criterion to evaluate the model's performance between original and synthetic data sets. We found that all performances of our proposed method are better than the traditional methods.