Research on sentiment analysis of Chinese online comments based on BERT-BiLSTM-DPCNN model

Wenxuan Cai; Songling Fu

doi:10.1117/12.3032869

5 July 2024 Research on sentiment analysis of Chinese online comments based on BERT-BiLSTM-DPCNN model

Wenxuan Cai, Songling Fu

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131842N (2024) https://doi.org/10.1117/12.3032869
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

With the popularity of social media and online comments, people chat and comment more frequently on social media. Due to the sparse and high-dimensional characteristics of the massive Chinese text itself, its semantic interpretation is also diverse and strong context-dependent, so accurate analysis of user emotion has become very important.Aiming at the problems of poor performance of traditional language models in dealing with long-distance dependencies and inadequate capture of significant features of text by classification models, a sentiment analysis model based on BERT-BiLSTMDPCNN is proposed. The model uses BERT pre-trained language model for text representation, BiLSTM sequence modeling and DPCNN hierarchical feature extraction to get the emotional polarity of the comment text. The experiment was carried out on the data set of comment text crawled from platforms such as Bilibili and Weibo. The experimental results show that the BERT-BiLSTM-DPCNN model has improved significantly, and the classification accuracy is 96.12%, which is better than other baseline models. It shows the effectiveness of the BERT-BiLSTM-DPCNN text sentiment analysis model.

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

Citation Download Citation

Wenxuan Cai and Songling Fu "Research on sentiment analysis of Chinese online comments based on BERT-BiLSTM-DPCNN model", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131842N (5 July 2024); https://doi.org/10.1117/12.3032869

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KEYWORDS

Data modeling

Feature extraction

Performance modeling

Analytical research

Convolution

Emotion

Machine learning

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