A Method for Constructing Supervised Time Topic Model Based on Variational Autoencoder

Abstract

Topic modeling is a probabilistic generation model to find the representative topic of a document and has been successfully applied to various document-related tasks in recent years. Especially in the supervised topic model and time topic model, many methods have achieved some success. The supervised topic model can learn topics from documents annotated with multiple labels and the time topic model can learn topics that evolve over time in a sequentially organized corpus. However, there are some documents with multiple labels and time-stamped in reality, which need to construct a supervised time topic model to achieve document-related tasks. There are few research papers on the supervised time topic model. To solve this problem, we propose a method for constructing a supervised time topic model. By analysing the generative process of the supervised topic model and time topic model, respectively, we introduce the construction process of the supervised time topic model based on variational autoencoder in detail and conduct preliminary experiments. Experimental results demonstrate that the supervised time topic model outperforms several state-of-the-art topic models.

Application of K -Means Clustering Algorithm in Energy Data Analysis

“…For the optimization of the center, it includes the maximum distance product algorithm, minimum variance optimization method, and maximum minimum similarity. In addition, it also combines LDA and other models to solve the problems of data space and semantic barriers [14,15].…”