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Approach for text classification based on the similarity measurement between normal cloud models.

Jin Dai1, Xin Liu1

  • 1College of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Thescientificworldjournal
|April 9, 2014
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Summary

This study introduces a novel text classifier, cloud concept jumping up (CCJU-TC), which uses cloud similarity for accurate text classification. CCJU-TC effectively converts qualitative concepts to quantitative data, outperforming traditional methods.

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Area of Science:

  • Data Mining
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Object similarity is central to data mining.
  • Natural language presents challenges due to inherent uncertainty.
  • Existing text classification methods can be improved for accuracy and adaptability.

Purpose of the Study:

  • To propose a novel text classifier, cloud concept jumping up (CCJU-TC), for improved text classification.
  • To address the uncertainty of natural language by employing cloud model similarity.
  • To enable efficient conversion between qualitative concepts and quantitative data in text analysis.

Main Methods:

  • Utilized similarity measurement between normal cloud models for text classification.
  • Developed the CCJU-TC classifier for qualitative-to-quantitative concept conversion.
  • Employed the Vector Space Model (VSM) to convert text sets into information tables.
  • Applied "concept jumping up" to aggregate concepts within categories.
  • Measured cloud similarity between test texts and category concepts for classification.

Main Results:

  • CCJU-TC successfully converts qualitative text concepts into quantitative data.
  • The classifier demonstrated adaptability to various text features.
  • CCJU-TC exhibited superior classification performance compared to traditional classifiers.
  • The "concept jumping up" mechanism effectively represents category concepts.

Conclusions:

  • CCJU-TC offers a robust approach to text classification by leveraging cloud model similarity.
  • The proposed method enhances adaptability and accuracy in text analysis.
  • CCJU-TC provides an effective solution for handling natural language uncertainty in classification tasks.