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Web content topic modeling using LDA and HTML tags.

Hamza H M Altarturi1, Muntadher Saadoon2, Nor Badrul Anuar1

  • 1Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Kuala Lumpur, Malaysia.

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Summary
This summary is machine-generated.

A new HTML Topic Model (HTM) improves topic coherence in web content analysis. Existing models underperform on web data, but HTM leverages HTML structure for better topic discovery and understanding.

Keywords:
Generative modelHTMHTML tagsHTML topic modelLDATopic modelingTopic models comparisonWeb content miningWeb topic modeling

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Area of Science:

  • Natural Language Processing
  • Data Mining
  • Web Content Analysis

Background:

  • Topic modeling is crucial for analyzing digital documents, but conventional models struggle with web content's unique structure.
  • Existing topic models underperform significantly on web data, leading to low topic quality and missed insights.

Purpose of the Study:

  • To propose an innovative topic model specifically designed for web content data.
  • To address the limitations of existing topic models in discovering coherent topics within HTML documents.

Main Methods:

  • Introduction of the HTML Topic Model (HTM), which incorporates HTML tags to interpret web page structure.
  • Experimental comparison of HTM against Latent Dirichlet Allocation (LDA) and its variants on web content data.

Main Results:

  • Existing topic models showed a performance drop of up to 20 times on web data compared to conventional documents.
  • The proposed HTM model achieved a 35% improvement in topic coherence over the LDA model.

Conclusions:

  • A specialized topic model is essential for effective web content analysis.
  • HTM demonstrates superior performance in uncovering coherent topics from web data by utilizing HTML structure.