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Position-Wise Gated Res2Net-Based Convolutional Network with Selective Fusing for Sentiment Analysis.

Jinfeng Zhou¹, Xiaoqin Zeng¹, Yang Zou¹

¹College of Computer and Information, Hohai University, Nanjing 210098, China.

Entropy (Basel, Switzerland)

|May 27, 2023

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This study introduces a novel convolutional neural network (CNN) for sentiment analysis (SA) that effectively captures multi-scale features. The proposed model enhances sentiment classification accuracy by addressing limitations in existing methods.

Area of Science:

Natural Language Processing
Artificial Intelligence
Machine Learning

Background:

Sentiment analysis (SA) is crucial in natural language processing.
Existing convolutional neural networks (CNNs) struggle with fixed-scale features and information loss.
A need exists for models that can synthesize flexible, multi-scale sentiment features.

Purpose of the Study:

To propose a new CNN model for SA that overcomes limitations of existing approaches.
To enhance sentiment classification accuracy by extracting multi-scale features and preserving local details.
To leverage residual network technology and attention mechanisms for improved SA.

Main Methods:

Developed a novel CNN model incorporating a position-wise gated Res2Net (PG-Res2Net) module and a selective fusing module.

Keywords:

Res2Net ResNet convolutional neural network deep neural networks sentiment analysis

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The PG-Res2Net module adaptively learns multi-scale sentiment features using multi-way convolution, residual connections, and position-wise gates.

The selective fusing module efficiently reuses and fuses learned features for prediction.

Main Results:

The proposed model demonstrated superior performance compared to baseline models across five datasets.
Achieved an improvement of up to 1.2% in performance in the best-case scenario.
Ablation studies and visualizations confirmed the model's effectiveness in extracting and fusing multi-scale sentiment features.

Conclusions:

The novel CNN model effectively extracts and fuses multi-scale sentiment features, outperforming existing methods.
The proposed architecture successfully addresses the loss of local detailed information in traditional CNNs.
This approach offers a significant advancement in the accuracy and capability of sentiment analysis.