Matrix Inter-joint Factorization-A New Approach for Topic Derivation in Twitter

Robertus Nugroho, Youliang Zhong, Jian Yang, Cecile Paris, Surya Nepal

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

7 Citations (Scopus)


Amongst all the social media platforms available, Twitter is rapidly becoming the main one used for communications about real-Time events. As a result, there is a lot of interest in monitoring Twitter and understanding the topics of conversations. However, the fact that tweets are short in content makes topics derivation a challenge, as most existing methods use content features only, sometimes integrated with limited interaction information. In this paper, we propose a novel method: Non-negative Matrix inter-joint Factorization (NMijF), in which we conduct co-factorization jointly over Twitter interaction features and content attributes within a single iterative-update process. We have conducted comprehensive experiments on real Twitter datasets and evaluated the performance of the proposed method, especially comparing it with the Joint Non-negative Matrix Factorization (joint-NMF) and Non-negative Matrix co-Factorization (NMcF) methods. Our experiment results show that the proposed NMijF method outperforms joint-NMF, NMcF and other advanced topic derivation methods in terms of Topic Coherence, Purity, Normalized Mutual Information and Precision-Recall.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015
Number of pages8
ISBN (Electronic)9781467372787
Publication statusPublished - 17 Aug 2015
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Big Data - New York City, United States
Duration: 27 Jun 20152 Jul 2015
Conference number: 4


ConferenceIEEE International Conference on Big Data
Abbreviated titleBigData
CountryUnited States
CityNew York City


  • Inter-Joint Factorization
  • Non-negative Matrix Factorization
  • Topic Derivation
  • Twitter


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