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Topic models provide a flexible and principled framework for exploring hiddenstructure in high-dimensional co-occurrence data and are commonly used naturallanguage processing (NLP) of text. In this paper, we design and implement aJava package, TopicModel4J, which contains 13 kinds of representativealgorithms for fitting topic models. The TopicModel4J in the Java programmingenvironment provides an easy-to-use interface for data analysts to run thealgorithms, and allow to easily input and output data. In addition, thispackage provides a few unstructured text preprocessing techniques, such assplitting textual data into words, lowercasing the words, preforminglemmatization and removing the useless characters, URLs and stop words.

TopicModel4J: A Java Package for Topic Models