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Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis.

Rania Albalawi1, Tet Hin Yeap1, Morad Benyoucef2

  • 1School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada.

Frontiers in Artificial Intelligence
|March 18, 2021
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Summary
This summary is machine-generated.

This study compares topic modeling methods for analyzing social media text. Latent Dirichlet Allocation and Non-negative Matrix Factorization are most effective for extracting meaningful topics from user-generated content.

Keywords:
natural language processingonline social networksshort texttopic modelinguser-generated content

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Area of Science:

  • Computational Linguistics
  • Data Science
  • Social Media Analytics

Background:

  • Online social networks generate vast amounts of user-generated text daily.
  • Extracting relevant information from this data is challenging for users.
  • Machine learning and natural language processing, particularly topic modeling, offer solutions.

Purpose of the Study:

  • To investigate topic modeling techniques for analyzing short, user-generated social media text.
  • To compare the performance of five common topic modeling methods.
  • To identify effective methods for discovering important topics in social media data.

Main Methods:

  • Evaluated Latent Semantic Analysis, Latent Dirichlet Allocation, Non-negative Matrix Factorization, Random Projection, and Principal Component Analysis.
  • Applied methods to two textual datasets of user-generated social media content.
  • Assessed performance using topic quality metrics and statistical measures (recall, precision, F-score, topic coherence).

Main Results:

  • Latent Dirichlet Allocation and Non-negative Matrix Factorization demonstrated superior performance.
  • These methods yielded more meaningful extracted topics.
  • Both methods achieved strong results in topic quality and evaluation metrics.

Conclusions:

  • Latent Dirichlet Allocation and Non-negative Matrix Factorization are recommended for topic modeling in short-text social media contexts.
  • The study provides practical insights into applying topic modeling to user-generated content.
  • Offers guidance for researchers utilizing these techniques.