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Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for

Lin Wang1, Zuqiang Meng1

  • 1School of Computer and Electronic Information, Guangxi University, Nanning 530004, China.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
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A novel multichannel two-dimensional convolutional neural network (MCNN-IFGS) improves Chinese sentiment analysis by using character-level encoding and group strategies for more efficient and comprehensive feature extraction, outperforming existing methods.

Area of Science:

  • Natural Language Processing
  • Deep Learning
  • Computational Linguistics

Background:

  • Recurrent Neural Networks (RNNs) and 1D-CNNs are common for Chinese sentiment analysis but suffer from inefficiency and limited feature utilization.
  • RNNs lack parallelization, while 1D-CNNs extract single-sample features, hindering full information exploitation.

Purpose of the Study:

  • To propose an efficient and effective method for Chinese sentiment analysis.
  • To overcome the limitations of existing RNN and 1D-CNN based approaches.

Main Methods:

  • Introduced a multichannel two-dimensional convolutional neural network based on interactive features and group strategy (MCNN-IFGS).
  • Employed character-based integer encoding for finer-grained information and introduced interactive features in character-level vectors.
Keywords:
feature mapping groupgroup strategyinteractive featuresmultichanneltwo-dimensional convolutional neural network

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  • Utilized group strategies to form feature mapping groups for enhanced learning and employed multichannel 2D-CNNs with varying kernel sizes for multi-scale feature extraction.

    • Main Results:

    • The proposed MCNN-IFGS method demonstrated superior performance on a Chinese sentiment analysis dataset.
    • Achieved better results compared to baseline and state-of-the-art methods.

    Conclusions:

    • MCNN-IFGS offers an improved approach to Chinese sentiment analysis.
    • Character-level encoding, interactive features, group strategies, and multichannel 2D-CNNs collectively enhance sentiment feature learning and model performance.