CNN-BiLSTM model based on multi-level and multi-scale feature extraction for sentiment analysis on social platforms

Feng Qian; Ruoyuan Zhang

doi:10.1117/12.3033195

5 July 2024 CNN-BiLSTM model based on multi-level and multi-scale feature extraction for sentiment analysis on social platforms

Feng Qian, Ruoyuan Zhang

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Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131841Y (2024) https://doi.org/10.1117/12.3033195
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In social platform sentiment analysis tasks, existing methods only consider sentiment features from a single pole and scale, which cannot fully exploit and utilize sentiment feature information, and the model performance is not satisfactory. To address this problem, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model with multi-level and multi-scale feature extraction is proposed. The model first uses the pre-trained Chinese word vector model and combines the embedding layer fine-tuning to obtain word-level features; then uses the multi-scale phrase-level feature representation module and the sentence-level feature representation module to obtain phrase-level and sentence level features, respectively, and in the multi-scale phrase-level feature representation module, uses convolutional networks with different convolutional kernel sizes to obtain phrase-level features at different scales; finally uses the multi-level feature The word-level features, phrase-level features at different scales and sentence-level features are fused to form multi-level joint features, which have more sentiment information compared with unipolar and single-scale features. Finally, the reliability of the proposed method is verified by simulation tests of sentiment classification on social platforms. The experimental results show that both multi-level and multi-scale features methods exhibit higher classification performance under different word vector size samples compared with commonly used sentiment classification algorithms

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Feng Qian and Ruoyuan Zhang "CNN-BiLSTM model based on multi-level and multi-scale feature extraction for sentiment analysis on social platforms", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131841Y (5 July 2024); https://doi.org/10.1117/12.3033195

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KEYWORDS

Feature extraction

Education and training

Analytical research

Convolution

Convolutional neural networks

Feature fusion

Computer simulations

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