A Comparative Study of Latent Semantics-based Anchor Word Selection Method for Separable Nonnegative Matrix Factorization

Naufal Khairil Imami, Hendri Murfi, Arie Wibowo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Topic detection is a process used to analyze words in a collection of textual data to determine the topics in the collection, how they relate to each other, and how these topics change from time to time. One of recent topic detection methods is Separable Nonnegative Matrix Factorization (SNMF) which uses the direct method to solve nonnegative matrix factorization using separable assumption. There are three stages in the SNMF method, which are, generating a word co-occurrence matrix, determining anchor words, and recover to get the matrix of word-topics. In this paper, we examine a latent semantics-based method to determine the anchor words for each topics. Our simulation shows that both latent semantic-based methods reach coherence scores comparable to the standard method; however, more efficient in running time.

Original languageEnglish
Title of host publicationBDET 2020 - 2020 2nd International Conference on Big Data Engineering and Technology
PublisherAssociation for Computing Machinery
Pages89-92
Number of pages4
ISBN (Electronic)9781450376839
DOIs
Publication statusPublished - 3 Jan 2020
Event2nd International Conference on Big Data Engineering and Technology, BDET 2020 - Singapore, Singapore
Duration: 3 Jan 20205 Jan 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2nd International Conference on Big Data Engineering and Technology, BDET 2020
CountrySingapore
CitySingapore
Period3/01/205/01/20

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Keywords

  • latent semantics
  • online news
  • separable nonnegative matrix vectorization
  • Topic detection
  • twitter

Cite this

Imami, N. K., Murfi, H., & Wibowo, A. (2020). A Comparative Study of Latent Semantics-based Anchor Word Selection Method for Separable Nonnegative Matrix Factorization. In BDET 2020 - 2020 2nd International Conference on Big Data Engineering and Technology (pp. 89-92). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3378904.3378906