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Manifold-based sparse representation for opinion mining.

Zohre Karimi1

  • 1School of Engineering, Damghan University, Damghan, Iran. z.karimi@du.ac.ir.

Scientific Reports
|September 23, 2023
PubMed
Summary

This study enhances opinion mining by developing a novel sparse manifold-based representation for user reviews. This approach significantly improves classification accuracy for sentiment analysis on large datasets.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Mining

Background:

  • Consumer opinions on products/services are vital business indicators.
  • Traditional text feature methods for opinion mining face challenges like high dimensionality and noise.
  • Existing nonlinear feature selection methods use nearest neighbor graphs, which can include diverse polarities.

Purpose of the Study:

  • To enhance feature representation for opinion mining.
  • To address limitations of classical text feature representation methods.
  • To propose a novel approach combining manifold assumption and sparse property for opinion representation.

Main Methods:

  • A new algorithm is proposed that exploits manifold assumption and sparse property as prior knowledge.

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  • It learns a graph representation of user reviews based on this prior knowledge.
  • Spectral properties of the learned graph are used to create a new feature space.

    • Main Results:

    • The proposed algorithm was tested on IMDB and Amazon review datasets.
    • It demonstrated considerable enhancements in F-measure and accuracy compared to state-of-the-art methods.
    • Highest accuracies of 99.15% (IMDB) and 91.97% (Amazon) were achieved using a linear SVM classifier.

    Conclusions:

    • The sparse manifold-based representation leads to significant advancements in opinion mining.
    • The method effectively learns intrinsic data structure, overcoming limitations of previous techniques.
    • Experimental results validate the effectiveness of the proposed approach and its underlying assumptions.