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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.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.
References
A users number report of sina weibo. In: https://finance.sina.com.cn/stock/usstock/c/2023-11-09/doc-imztzksh0045805.shtml. Accessed 09 Nov 2023
Zhang W, Li X, Deng Y, Bing L, Lam W (2023) A survey on aspect-based sentiment analysis: Tasks, methods, and challenges. IEEE Trans Knowl Data Eng 35(11):11019–11038. https://doi.org/10.1109/TKDE.2022.3230975
Saymon Ahammad M, Sinthia SA, Muaj Chowdhury M, Asif NAA, Nurul Afsarikram M (2024) Sentiment analysis of various ride sharing applications reviews: A comparative analysis between deep learning and machine learning algorithms. In: International conference on computational intelligence in data science, Springer, pp 434–448. https://doi.org/10.1007/978-3-031-69986-3_33
Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations(ICLR), University of Amsterdam. https://doi.org/10.48550/arXiv.1609.02907
Kim Y (2014) Convolutional Neural Networks for Sentence Classification. Eprint Arxiv, New York University. https://doi.org/10.3115/v1/D14-1181
Zeng R, Liu H, Peng S, Cao L, Yang A, Zong C, Zhou G (2023) Cnn-based broad learning for cross-domain emotion classification. Tsinghua Sci Technol 28(2):360–369. https://doi.org/10.26599/TST.2022.9010007
Huang F, Li X, Yuan C, Zhang S, Qiao S (2021) Attention-emotion-enhanced convolutional lstm for sentiment analysis. IEEE Transactions on neural networks and learning systems. 33(9):4332–4345. https://doi.org/10.1109/TNNLS.2021.3056664
Wu O, Yang T, Li M, Li M (2020) Two-level lstm for sentiment analysis with lexicon embedding and polar flipping. IEEE Transactions on Cybernetics. 52(5):3867–3879. https://doi.org/10.1109/TCYB.2020.3017378
Zhou J, Huang JX, Hu QV, He L (2020) Sk-gcn: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowl-Based Syst 205(3):106292. https://doi.org/10.1016/j.knosys.2020.106292
Lai Y, Zhang L, Han D, Zhou R, Wang G (2020) Fine-grained emotion classification of chinese microblogs based on graph convolution networks. World Wide Web. 23(4):2771–2787. https://doi.org/10.1007/s11280-020-00803-0
Machuca CR, Gallardo C, Toasa RM (2021) Twitter sentiment analysis on coronavirus: Machine learning approach. J Phys: Conf Ser 1828(1):012104. https://doi.org/10.1088/1742-6596/1828/1/012104
Ren R, Wu DD, Liu T (2018) Forecasting stock market movement direction using sentiment analysis and support vector machine. IEEE Syst J 13(1):760–770. https://doi.org/10.1109/JSYST.2018.2794462
Mendon S, Dutta P, Behl A, Lessmann S (2021) A hybrid approach of machine learning and lexicons to sentiment analysis: enhanced insights from twitter data of natural disasters. Inf Syst Front 23(5):1145–1168. https://doi.org/10.1007/s10796-021-10107-x
Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inform Retrieval 2(1–2):1–135. https://doi.org/10.1561/1500000011
Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: A survey. arXiv e-prints. 8(4). https://doi.org/10.1002/WIDM.1253
Gan C, Wang L, Zhang Z, Wang Z (2020) Sparse attention based separable dilated convolutional neural network for targeted sentiment analysis. Knowl-Based Syst 188:104827. https://doi.org/10.1016/j.knosys.2019.06.035
Socher R, Perelygin A, Wu J, Chuang J, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1631–1642. https://doi.org/10.48550arXiv:1609.02907
Abdul-Mageed M, Ungar L (2017) Emonet: Fine-grained emotion detection with gated recurrent neural networks. In: Proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: Long Papers), pp 718–728. https://doi.org/10.18653/v1/P17-1067
Sachin S, Tripathi A, Mahajan N, Aggarwal S, Nagrath P (2020) Sentiment analysis using gated recurrent neural networks. SN Comput Sci 1:1–13. https://doi.org/10.1007/S42979-023-02168-3
Baziotis C, Pelekis N, Doulkeridis C (2017) Datastories at semeval-2017 task 4: Deep lstm with attention for message-level and topic-based sentiment analysis. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017), pp 747–754. https://doi.org/10.18653/v1/S17-2126
Zhou J, Lu Y, Dai H, Wang H, Xiao H (2019) Sentiment analysis of chinese microblog based on stacked bidirectional lstm. IEEE Access. 7:38856–38866. https://doi.org/10.1109/ACCESS.2019.2905048
Qian Q, Huang M, Lei J, Zhu X (2017) Linguistically regularized lstms for sentiment classification. In: Proceedings of the 55th annual meeting of the association for computational linguistics (vol 1: Long Papers), pp. 1679–1689. https://doi.org/10.18653/v1/P17-1154
Zheng Z, Li LI, Jing C (2018) Deeply hierarchical bi-directional lstm for sentiment classification. Comput Sci 45(8):213–217. https://doi.org/10.11896/j.issn.1002-137X.2018.08.038
Wang J, Yang Y, Wang X (2018) Reslcnn model for short text classification. J softw 28(s2):61–69. http://www.jos.org.cn/1000-9825/17019.htm
Gonzalez JA, Hurtado L-F, Pla F (2021) Twilbert: Pre-trained deep bidirectional transformers for spanish twitter. Neurocomputing 426:58–69. https://doi.org/10.1016/j.neucom.2020.09.078
Zhao A, Yu Y (2021) Knowledge-enabled bert for aspect-based sentiment analysis. Knowl-Based Syst 227:107220. https://doi.org/10.1016/j.knosys.2021.107220
Marcheggiani D, Titov I (2017) Encoding sentences with graph convolutional networks for semantic role labeling. In: Proceedings of the 2017 conference on empirical methods in natural language processing. https://doi.org/10.18653/v1/D17-1159
Yifu L, Jin R, Luo Y (2018) Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (seg-gcrns). J Am Med Inform Assoc 26(3):262–268. https://doi.org/10.1093/jamia/ocy157
Bastings J, Titov I, Aziz W, Marcheggiani D, Simaan K (2017) Graph convolutional encoders for syntax-aware neural machine translation. In: Proceedings of the 2017 conference on empirical methods in natural language processing (EMNLP), pp 1957–1967. https://doi.org/10.48550/arXiv.1806.08804
Zhao P, Hou L, Wu O (2020) Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification. Knowl-Based Syst 193(1–10):105443. https://doi.org/10.1016/j.knosys.2019.105443
Hou Y, Zhuang X, Zhang Y, Zhang L (2024) Intrinsic dependency graph convolutional networks for aspect level sentiment analysis. In: 2024 9th International conference on computer and communication systems (ICCCS), pp 1369–1374. https://doi.org/10.1109/ICCCS61882.2024.10603265
Yi J, Wu X, Liu X (2024) Context-guided and syntactic augmented dual graph convolutional network for aspect-based sentiment analysis. In: IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 12401–12405. https://doi.org/10.1109/ICASSP48485.2024.10448386
Kalchbrenner N, Grefenstette E, Blunsom P (2017) A convolutional neural network for modelling sentences. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 1957–1967. https://doi.org/10.3115/v1/P14-1062
Ying R, You J, Morris C, Ren X, Hamilton WL, Leskovec J (2018) Hierarchical graph representation learning with differentiable pooling. Adv Neural Inform Process Syst 31. https://doi.org/10.48550/arXiv.1806.08804
Gao H, Ji S (2019) Graph u-nets. In: International conference on machine learning, pp 2083–2092. https://doi.org/10.48550/arXiv.1905.05178
China Computer Federation Technical Committee on Natural Language Processing (2013). The 2nd Conference on Natural Language Processing and Chinese Computing. figshare http://tcci.ccf.org.cn/conference/2013/dldoc/evdata02.zip
Wen S, Wan X (2014) Emotion classification in microblog texts using class sequential rules. In: Proceedings of the AAAI conference on artificial intelligence, vol 28. figshare https://doi.org/10.1609/aaai.v28i1.8709
Jiang F, Liu YQ, Luan HB, Sun JS, Zhu X, Zhang M, Ma SP (2015) Microblog sentiment analysis with emoticon space model. J Comput Sci Technol 30(5):1120–1129. https://doi.org/10.1007/s11390-015-1587-1
Ye P, Kumar J, Kang L, Doermann D (2012) Unsupervised feature learning framework for no-reference image quality assessment. In: 2012 IEEE Conference on computer vision and pattern recognition, pp 1098–1105. https://doi.org/10.48550/arXiv.1603.03827
Lee JY, Dernoncourt F (2016) Sequential short-text classification with recurrent and convolutional neural networks. In: Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics (NAACL): human language technologies, pp 515–520. https://doi.org/10.18653/v1/N16-1062
Zhou L, Zhang Z, Zhao P, Yang L (2023) Microblog sentiment analysis based on deep memory network with structural attention. Complex Intell Syst 9(3):3071–3083. https://doi.org/10.1007/s40747-022-00904-5
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).
Author information
Authors and Affiliations
Contributions
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.
Corresponding author
Ethics declarations
Competing interests
All authors disclosed no relevant relationships.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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
Accepted:
Published:
DOI: https://doi.org/10.1007/s10489-024-06102-9