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This study introduces a new supervised method for sentence-level sentiment analysis (SLSA) using gradual machine learning (GML). GML outperforms deep learning models by addressing non-i.i.d data challenges in sentiment analysis.

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Area of Science:

  • Natural Language Processing
  • Machine Learning

Background:

  • Sentence-level sentiment analysis (SLSA) typically uses deep learning models.
  • Deep learning models often struggle with real-world data due to the i.i.d. assumption, where training and target data distributions differ.

Purpose of the Study:

  • To propose a supervised solution for SLSA within the non-i.i.d. paradigm of gradual machine learning (GML).
  • To improve SLSA performance by addressing distribution shifts between training and target data.

Main Methods:

  • Leveraging deep neural networks (DNNs) for supervised deep feature extraction.
  • Constructing a factor graph for iterative knowledge conveyance based on extracted features.
  • Employing a polarity classifier and a binary semantic network for similarity and relation extraction.

Main Results:

  • The proposed GML approach achieved state-of-the-art performance on all benchmark datasets.
  • GML, with DNN feature extraction, outperformed pure DNN solutions in SLSA.

Conclusions:

  • Gradual machine learning (GML) offers a robust solution for SLSA, particularly in non-i.i.d. scenarios.
  • Integrating DNNs for feature extraction within GML enhances sentiment analysis capabilities beyond traditional deep learning methods.