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LDA filter: A Latent Dirichlet Allocation preprocess method for Weka.

P Celard1,2,3, A Seara Vieira1,2,3, E L Iglesias1,2,3

  • 1Computer Science Dept., Univ. of Vigo, Escuela Superior de Ingeniería Informática, Ourense, Spain.

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This study introduces a new document representation method using Latent Dirichlet Allocation (LDA) topic probabilities. It achieves comparable accuracy to Bag of Words (BoW) but significantly speeds up text classification processing times.

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Machine Learning

Background:

  • Traditional text representation methods like Bag of Words (BoW) are widely used in document classification.
  • Latent Dirichlet Allocation (LDA) is a generative statistical model for discovering latent topics within a corpus.
  • Representing documents based on LDA topic distributions offers a potential alternative to existing methods.

Purpose of the Study:

  • To propose and evaluate a novel document representation technique leveraging LDA topic probabilities.
  • To assess the impact of this LDA-based representation on the performance of various classification algorithms.
  • To compare the proposed method against the standard Bag of Words (BoW) representation.

Main Methods:

  • Developed a new text representation filter based on the probability of a document belonging to each LDA-generated topic.
  • Integrated the filter as an extension within the Weka software environment.
  • Evaluated the filter using multiple classifiers (SVM, k-NN, Naive Bayes) on diverse document corpora (OHSUMED, Reuters, 20Newsgroup, Yahoo! Answers, YELP, TREC Genomics).

Main Results:

  • The LDA-based document representation achieved classification accuracy comparable to the Bag of Words (BoW) method.
  • A significant reduction in classification processing times was observed when using the proposed LDA-based representation.
  • Performance was consistent across various classifiers and diverse datasets.

Conclusions:

  • The proposed LDA topic probability-based document representation is an effective alternative to BoW.
  • This method offers a valuable trade-off between accuracy and computational efficiency in text classification.
  • The Weka extension provides a practical tool for implementing this advanced text representation technique.