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Fine grained sentiment analysis on microblogs based on graph convolution and self attention graph pooling

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Abstract

Microblog is one of the most popular social media platforms in China. Fine grained sentiment analysis of Chinese microblog comments has attracted much attention. Graph Convolutional Neural Network (GCN) has been broadly used in sentiment analysis but still suffers from emotion misclassification due to the complexity and diversity of Chinese microblogs’ syntax structures. To address the issue, we propose a graph pooling method based on self-attention mechanism, namely, AGMPool. The AGMPool pooling method uses graph convolution to calculate its attention score for each graph node and then to filter out nodes with excessive useless information in the graph topology according to these scores, which effectively improves the performance of fine-grained sentiment analysis through GCN. In addition, for better understanding of diverse syntax structures of Chinese microblogs, we propose a microblog fine grained sentiment analysis model, namely, LMG-AGMPool, which combines GCN with the AGMPool pooling method and extracts emotional features based on the syntax structures of text and the importance of words in text. The experimental results indicate that the LMG-AGMPool model has better performance than the traditional methods and the deep learning methods in fine grained sentiment analysis.

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Availability of data and materials

The authors confirm that the data supporting the findings of this study are available within the article.

Code availability

Our code is not currently open-source, but we plan to release it to the public after the final approval of our paper.

Notes

  1. https://github.com/fxsjy/jieba

  2. http://ltp.ai

  3. http://tcci.ccf.org.cn/conference/2013/dldoc/ev02.pdf

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Funding

This work is supported by the General Project of Liaoning Provincial Department of Education Science Research (LJKMZ20220838, LJKZ0481), 2020 Industrial Internet Innovation and Development Project Overall Testing of Intelligent Applications Based on Industrial Internet Platform (TC2008033-1-1-1).

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Dr. Li and Dr. Niu selected the direction of the paper and downloaded the dataset,and provided guidance in the experimental section. Zhou proposed a pooling method based on self attention mechanism, as well as an LMG-AGMPool model that combines this pooling method, and conducted experiments on the server. Zhao organized and visualized the experimental data. All authors read and approved the final manuscript.

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Correspondence to Yuanyuan Li.

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Li, Y., Zhou, B., Niu, Y. et al. Fine grained sentiment analysis on microblogs based on graph convolution and self attention graph pooling. Appl Intell 55, 92 (2025). https://doi.org/10.1007/s10489-024-06102-9

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