A high-reproducibility and high-accuracy method for automated topic classification
This work addresses the need for more reliable automated topic classification in big data text analysis systems, representing an incremental improvement over existing methods.
The authors tackled the problem of unreliable parameter inference in Latent Dirichlet Allocation (LDA) for topic classification, proposing a new algorithm that achieves high reproducibility and accuracy, with high computational efficiency, as demonstrated on the English Wikipedia dataset.
Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent search, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state-of-the-art in topic classification. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results which are not accurate in inferring the most suitable model parameters. Adapting approaches for community detection in networks, we propose a new algorithm which displays high-reproducibility and high-accuracy, and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure. Our algorithm promises to make "big data" text analysis systems more reliable.