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CharAs-CBert: Character Assist Construction-Bert Sentence Representation Improving Sentiment Classification.

Bo Chen1, Weiming Peng1, Jihua Song1

  • 1School of Artificial Intelligence, Beijing Normal University, No. 19, Xinjiekouwai St., Haidian District, Beijing 100875, China.

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Summary
This summary is machine-generated.

This study introduces character-assisted construction-Bert (CharAs-CBert) for improved sentiment text classification. The novel method enhances semantic capture by integrating character and construction information, boosting accuracy.

Keywords:
character vectorconstruction vectorinternal structure informationsentence representationsentiment classification

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Traditional sentence representation methods often lose global/contextual semantics and word structure.
  • This limitation hinders accurate sentiment text classification.

Purpose of the Study:

  • To propose a novel sentence representation method, character-assisted construction-Bert (CharAs-CBert), for enhanced sentiment text classification.
  • To improve the accuracy of sentiment analysis by addressing limitations in existing methods.

Main Methods:

  • Generating effective construction vectors to disambiguate words and capture contextual semantics.
  • Introducing character feature vectors to leverage internal sentence structure for improved semantic representation.
  • Combining character information, word information, and construction vectors for robust sentence representation.

Main Results:

  • The CharAs-CBert method demonstrated validity and reliability on benchmark datasets like ACL-14 and SemEval 2014.
  • Achieved high performance metrics, including an F1 score of 87.54% and an accuracy (ACC) of 92.88% on the ACL14 dataset.

Conclusions:

  • The proposed CharAs-CBert method significantly improves sentence representation for sentiment text classification.
  • Integrating character and construction information enhances the capture of both local and global semantics, leading to superior accuracy.