Comparative study of original recover and recover KL in separable non-negative matrix factorization for topic detection in Twitter

R. D. Prabandari, Hendri Murfi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

An increasing amount of information on social media such as Twitter requires an efficient way to find the topics so that the information can be well managed. One of an automated method for topic detection is separable non-negative matrix factorization (SNMF). SNMF assumes that each topic has at least one word that does not appear on other topics. This method uses the direct approach and gives polynomial-time complexity, while the previous methods are iterative approaches and have NP-hard complexity. There are three steps of SNMF algorithm, i.e. constructing word co-occurrences, finding anchor words, and recovering topics. In this paper, we examine two topic recover methods, namely original recover that is using algebraic manipulation and recover KL that using probability approach with Kullback-Leibler divergence. Our simulations show that recover KL provides better accuracies in term of topic recall than original recover.

Original languageEnglish
Title of host publicationInternational Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016
Subtitle of host publicationProceedings of the 2nd International Symposium on Current Progress in Mathematics and Sciences 2016
EditorsKiki Ariyanti Sugeng, Djoko Triyono, Terry Mart
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735415362
DOIs
Publication statusPublished - 10 Jul 2017
Event2nd International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016 - Depok, Jawa Barat, Indonesia
Duration: 1 Nov 20162 Nov 2016

Publication series

NameAIP Conference Proceedings
Volume1862
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2nd International Symposium on Current Progress in Mathematics and Sciences 2016, ISCPMS 2016
CountryIndonesia
CityDepok, Jawa Barat
Period1/11/162/11/16

Keywords

  • Kullback-Leibler
  • NMF
  • Twitter
  • separable NMF
  • topic detection

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