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Topic Subject Creation Using Unsupervised Learning for Topic Modeling

Author

Listed:
  • Rashid Mehdiyev
  • Jean Nava
  • Karan Sodhi
  • Saurav Acharya
  • Annie Ibrahim Rana

Abstract

We address the problem of topic mining and labelling in the domain of retail customer communications to summarize the subject of customers inquiries. The performance of two popular topic mining algorithms - Non-Negative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA) – were compared, and a novel method to assign topic subject labels to the customer inquiries in an automated way was proposed. Experiments using a retailer’s call center data verify the efficacy and efficiency of the proposed topic labelling algorithm. Furthermore, the evaluation of results from both the algorithms seems to indicate the preference of using Non-Negative Matrix Factorization applied to short text data.

Suggested Citation

  • Rashid Mehdiyev & Jean Nava & Karan Sodhi & Saurav Acharya & Annie Ibrahim Rana, 2020. "Topic Subject Creation Using Unsupervised Learning for Topic Modeling," Computer and Information Science, Canadian Center of Science and Education, vol. 13(3), pages 1-57, August.
    • Handle: RePEc:ibn:cisjnl:v:13:y:2020:i:3:p:57
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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