TopicModel4J: A Java Package for Topic Models
This provides a convenient tool for data analysts working in Java environments, but it is incremental as it packages existing methods.
The authors tackled the lack of a comprehensive Java package for topic modeling by developing TopicModel4J, which includes 13 representative algorithms and text preprocessing tools, resulting in an easy-to-use interface for data analysts.
Topic models provide a flexible and principled framework for exploring hidden structure in high-dimensional co-occurrence data and are commonly used natural language processing (NLP) of text. In this paper, we design and implement a Java package, TopicModel4J, which contains 13 kinds of representative algorithms for fitting topic models. The TopicModel4J in the Java programming environment provides an easy-to-use interface for data analysts to run the algorithms, and allow to easily input and output data. In addition, this package provides a few unstructured text preprocessing techniques, such as splitting textual data into words, lowercasing the words, preforming lemmatization and removing the useless characters, URLs and stop words.