Skip to main content
SpringerLink
Log in

Modeling Tweet Dependencies with Graph Convolutional Networks for Sentiment Analysis

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Nowadays, individuals spend significant time on online social networks and microblogging websites, consuming news and expressing their opinions and viewpoints on various topics. It is an excellent source of data for various data mining applications, such as sentiment analysis. Mining this type of data presents several challenges, including the posts’ short length and informal language. On the other hand, microblog posts contain a high degree of interdependence, which can help to improve sentiment classification based on text. This data can be represented as a graph, with nodes representing posts and edges representing the various relationships between them. By using recently developed deep learning models for graph structures, this approach enables efficient sentiment analysis of microblog posts. This paper utilizes graphs to represent microblog posts and their various relationships, such as user, friendship, hashtag, sentimental similarity, textual similarity, and common friends. It then employs graph neural networks to perform context-aware sentiment analysis. To make use of the knowledge contained in multiple graphs, we propose a stacking model that simultaneously employs multiple graph types. The findings demonstrate the relevance of sociological theories to the analysis of social media. Experimental results on HCR (a real-world Twitter sentiment analysis dataset), indicate that the proposed approach outperforms baselines and state-of-the-art models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. https://research.google.com/colaboratory/

  2. https://bitbucket.org/speriosu/updown/wiki/Home

References

  1. Cambria E, Poria S, Gelbukh A, Thelwall M. Sentiment analysis is a big suitcase. IEEE Intell Syst. 2017;32(6):74–80.

    Article  Google Scholar 

  2. Li D, Wang Y, Madden A, Ding Y, Tang J, Sun GG, et al. Analyzing stock market trends using social media user moods and social influence. J Assoc Inf Sci Technol. 2019;70(9):1000–13.

    Article  Google Scholar 

  3. Keramatfar A, Amirkhani H. Bibliometrics of sentiment analysis literature. J Inf Sci. 2019;45(1):3–15.

    Article  Google Scholar 

  4. Hussain A, Cambria E, Poria S, Hawalah AYA, Herrera F. Information fusion for affective computing and sentiment analysis. Inf Fusion. 2021;71:97–8.

    Article  Google Scholar 

  5. Zou X, Yang J, Zhang J. Microblog sentiment analysis using social and topic context. PloS one. 2018;13(2):e0191163.

  6. Hu X, Tang L, Tang J, Liu H. Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the sixth ACM international conference on Web search and data mining. Rome, Italy; 2013. p. 537–46.

  7. Sánchez-Rada JF, Iglesias CA. Social context in sentiment analysis: Formal definition, overview of current trends and framework for comparison. Inf Fusion. 2019;52:344–56.

    Article  Google Scholar 

  8. Feng S, Wang Y, Liu L, Wang D, Yu G. Attention based hierarchical LSTM network for context-aware microblog sentiment classification. World Wide Web. 2019;22(1):59–81.

    Article  Google Scholar 

  9. Abelson RP. Whatever became of consistency theory? Pers Soc Psychol Bull. 1983;9(1):37–54.

    Article  Google Scholar 

  10. McPherson M, Smith-Lovin L, Cook JM. Birds of a feather: homophily in social networks. Annu Rev Sociol. 2001;27(1):415–44.

    Article  Google Scholar 

  11. Bollen J, Gonçalves B, Ruan G, Mao H. Happiness is assortative in online social networks. Artif Life. 2011;17(3):237–51.

    Article  Google Scholar 

  12. Hatfield E, Cacioppo JT, Rapson RL. Emotional contagion. Curr Dir Psychol Sci. 1993;2(3):96–100.

    Article  Google Scholar 

  13. Kramer ADI, Guillory JE, Hancock JT. Experimental evidence of massive-scale emotional contagion through social networks. Proc Natl Acad Sci. 2014;111(24):8788–90.

    Article  Google Scholar 

  14. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. In: International Conference on Learning Representations. Arizona, USA; 2013. p. 1–12.

  15. Pennington J, Socher R, Manning CD. Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Doha, Qatar; 2014. p. 1532–43.

  16. Cer D, Yang Y, Kong S-Y, Hua N, Limtiaco N, St. John R, et al. Universal sentence encoder for English. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Brussels, Belgium: Association for Computational Linguistics; 2018. p. 169–74.

  17. Perozzi B, Al-Rfou R, Skiena S. DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, USA: Association for Computing Machinery; 2014. p. 701–10.

  18. Hou Y, Zhang J, Cheng J, Ma K, Ma RT, Chen H, et al. Measuring and improving the use of graph information in graph neural networks. In: International Conference on Learning Representations. New Orleans, USA; 2019. p. 1–16.

  19. Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations. Toulon, France; 2017. p. 1–14.

  20. Zhang M, Chen Y. Link prediction based on graph neural networks. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montréal, Canada; 2018. p. 5171–81.

  21. Schlichtkrull M, Kipf TN, Bloem P, van den Berg R, Titov I, Welling M. Modeling relational data with graph convolutional networks. In: The Semantic Web. Monterey, USA. 2018. p. 593–607.

  22. Keramatfar A, Amirkhani H, Jalaly Bidgoly A. Multi-thread hierarchical deep model for context-aware sentiment analysis. J Inf Sci. 2021;1–12.

  23. Shoeb M, Ahmed J. Sentiment analysis and classification of tweets using data mining. Int Res J Eng Technol. 2017;4(12):1471–4.

    Google Scholar 

  24. Khatua A, Khatua A, Cambria E. Predicting political sentiments of voters from Twitter in multi-party contexts. Appl Soft Comput. 2020;97:106743.

  25. Sarkar K. A stacked ensemble approach to Bengali sentiment analysis. In: Intelligent Human Computer Interaction. Daegu, Korea; 2020. p. 102–11.

  26. Rani S. Hybrid model using stack-based ensemble classifier and dictionary classifier to improve classification accuracy of Twitter sentiment analysis. Int J Emerg Trends Eng Res. 2020;8(7):2893–900.

    Article  MathSciNet  Google Scholar 

  27. Taboada M, Brooke J, Tofiloski M, Voll K, Stede M. Lexicon-based methods for sentiment analysis. Comput Linguist. 2011;37(2):267–307.

    Article  Google Scholar 

  28. Stone PJ, Hunt EB. A computer approach to content analysis: studies using the General Inquirer system. In: Proceedings of the May 21–23, 1963 spring joint computer conference. Detroit, USA: Association for Computing Machinery; 1963. p. 241–56.

  29. Baccianella S, Esuli A, Sebastiani F. SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: European Language Resources Association (ELRA). 2010. p. 2200–4.

  30. Strapparava C, Valitutti A. Wordnet affect: an affective extension of wordnet. Lisbon, Portugal: Lrec; 2004. p. 1083–1086.

  31. Cambria E, Li Y, Xing FZ, Poria S, Kwok K. SenticNet 6: Ensemble application of symbolic and subsymbolic AI for sentiment analysis. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 2020. p. 105–14.

  32. Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A. Sentiment strength detection in short informal text. J Am Soc Inf Sci Technol. 2010;61(12):2544–58.

    Article  Google Scholar 

  33. Hu M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. Seattle, USA; 2004. p. 168–77.

  34. Jurek A, Mulvenna MD, Bi Y. Improved lexicon-based sentiment analysis for social media analytics. Secur Inform. 2015;4(1):1–13.

    Article  Google Scholar 

  35. Gupta I, Joshi N. Enhanced twitter sentiment analysis using hybrid approach and by accounting local contextual semantic. J Intell Syst. 2020;29(1):1611–25.

    Article  Google Scholar 

  36. Zhang L, Wang S, Liu B. Deep learning for sentiment analysis: a survey. WIREs Data Min Knowl Discov. 2018;8(4):e1253.

  37. Despotovic V, Tanikic D. Sentiment analysis of microblogs using multilayer feed-forward artificial neural networks. Comput Inform. 2017;36(5):1127–42.

    Article  Google Scholar 

  38. Vassilev A. Bowtie-a deep learning feedforward neural network for sentiment analysis. In: International Conference on Machine Learning, Optimization, and Data Science. Cham: Springer; 2019. p. 360–71.

  39. Ma Y, Peng H, Cambria E. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Thirty-second AAAI conference on artificial intelligence. New Orleans, USA; 2018. p. 5876–5883.

  40. Shin B, Lee T, Choi JD. Lexicon integrated CNN models with attention for sentiment analysis. In: 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Copenhagen, Denmark; 2017. p. 149–58.

  41. Akhtar MS, Ekbal A, Cambria E. How intense are you? Predicting intensities of emotions and sentiments using stacked ensemble. IEEE Comput Intel Mag. 2020;15(1):64–75.

    Article  Google Scholar 

  42. Behera RK, Jena M, Rath SK, Misra S. Co-LSTM: convolutional LSTM model for sentiment analysis in social big data. Inf Process Manag. 2021;58(1):102435.

  43. Merello S, Ratto AP, Oneto L, Cambria E, editors. Ensemble application of transfer learning and sample weighting for stock market prediction. In: 2019 International Joint Conference on Neural Networks (IJCNN). 2019.

  44. Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E. Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies. San Diego, USA; 2016. p. 1480–9.

  45. Wang Y, Huang M, Zhu X, Zhao L. Attention-based LSTM for aspect-level sentiment classification. In: Conference on empirical methods in natural language processin. g2016. p. 606–15.

  46. Kardakis S, Perikos I, Grivokostopoulou F, Hatzilygeroudis I. Examining attention mechanisms in deep learning models for sentiment analysis. Appl Sci. 2021;11(9):1–14.

    Article  Google Scholar 

  47. Liu Q, Zhang H, Zeng Y, Huang Z, Wu Z. Content attention model for aspect based sentiment analysis. In: Proceedings of the 2018 World Wide Web Conference. Lyon, France; 2018. p. 1023–32.

  48. Maas A, Daly RE, Pham PT, Huang D, Ng AY, Potts C. Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Associationfor Computational Linguistics. Portland, USA; 2011. p. 142–50.

  49. Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B. Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2014. p. 1555–65.

  50. Li Y, Pan Q, Yang T, Wang S, Tang J, Cambria E. Learning word representations for sentiment analysis. Cognit Comput. 2017;9(6):843–51.

    Article  Google Scholar 

  51. Naderalvojoud B, Sezer EA. Sentiment aware word embeddings using refinement and senti-contextualized learning approach. Neurocomputing. 2020;405:149–60.

    Article  Google Scholar 

  52. Poria S, Majumder N, Hazarika D, Cambria E, Gelbukh A, Hussain A. Multimodal sentiment analysis: addressing key issues and setting up the baselines. IEEE Intell Syst. 2018;33(6):17–25.

    Article  Google Scholar 

  53. Karimvand AN, Chegeni RS, Basiri ME, Nemati S. Sentiment analysis of Persian instagram post: a multimodal deep learning approach. In: 7th International Conference on Web Research (ICWR). Tehran, Iran; 2021. p. 137–41.

  54. Peng W, Hong X, Zhao G. Adaptive modality distillation for separable multimodal sentiment analysis. IEEE Intell Syst. 2021;36(3):82–9.

    Article  Google Scholar 

  55. Li W, Zhu L, Cambria E. Taylor’s theorem: a new perspective for neural tensor networks. Knowl Based Syst. 2021;228:107258.

  56. Severyn A, Moschitti A. Twitter sentiment analysis with deep convolutional neural networks. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. Santiago, Chile; 2015. p. 959–62.

  57. Cliche M. BB_twtr at SemEval-2017 Task 4: Twitter sentiment analysis with CNNs and LSTMs. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). Vancouver, Canada: Association for Computational Linguistics; 2017. p. 573–80.

  58. Barnes J, Velldal E, Øvrelid L. Improving sentiment analysis with multi-task learning of negation. Nat Lang Eng. 2021;27(2):249–69.

    Article  Google Scholar 

  59. Majumder N, Poria S, Peng H, Chhaya N, Cambria E, Gelbukh A. Sentiment and sarcasm classification with multitask learning. IEEE Intell Syst. 2019;34(3):38–43.

    Article  Google Scholar 

  60. Yang Li, Amirmohammad Kazameini, Yash Mehta, Cambria E. Multitask learning for emotion and personality detection. IEEE Trans Affecti Comput. 2021;1(1):1–8.

  61. Dashtipour K, Gogate M, Li J, Jiang F, Kong B, Hussain A. A hybrid Persian sentiment analysis framework: integrating dependency grammar based rules and deep neural networks. Neurocomputing. 2019;380:1–10.

    Article  Google Scholar 

  62. Zhao P, Hou L, Wu O. Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification. Knowl Based Syst. 2020;193:105443.

  63. Jin Z, Zhao X, Liu Y. Heterogeneous graph network embedding for sentiment analysis on social media. Cognit Comput. 2021;13(1):81–95.

    Article  Google Scholar 

  64. Zhou J, Huang JX, Hu QV, He L. SK-GCN: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowl Based Syst. 2020;205:106292.

  65. Zhu X, Zhu L, Guo J, Liang S, Dietze S. GL-GCN: Global and local dependency guided graph convolutional networks for aspect-based sentiment classification. Expert Syst Appl. 2021;186:115712.

  66. Liao W, Zeng B, Liu J, Wei P, Cheng X, Zhang W. Multi-level graph neural network for text sentiment analysis. Comput Electr Eng. 2021;92:107096.

  67. Lei J, Zhang Q, Wang J, Luo H. BERT based hierarchical sequence classification for context-aware microblog sentiment analysis. In: International Conference on Neural Information Processing. 2019. p. 376–86.

  68. Sairamya NJ, Susmitha L, Thomas George S, Subathra MSP. Chapter 12 - Hybrid approach for classification of electroencephalographic signals using time–frequency images with wavelets and texture features. In: Hemanth DJ, Gupta D, Balas VE, editors. Intelligent Data Analysis for Biomedical Applications. Cambridge: Academic Press; 2019. p. 253–73.

    Chapter  Google Scholar 

  69. Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge, USA: MIT press; 2016.

    MATH  Google Scholar 

  70. Eickenberg M, Gramfort A, Varoquaux G, Thirion B. Seeing it all: Convolutional network layers map the function of the human visual system. Neuroimage. 2017;152(1):184–94.

    Article  Google Scholar 

  71. Kuzovkin I, Vicente R, Petton M, Lachaux J-P, Baciu M, Kahane P, et al. Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex. Commun Biol. 2018;1(1):107.

    Article  Google Scholar 

  72. Zhang S, Tong H, Xu J, Maciejewski R. Graph convolutional networks: a comprehensive review. Comput Soc Netw. 2019;6(1):11.

    Article  Google Scholar 

  73. Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst. 2019;32(1):4–24.

    Article  MathSciNet  Google Scholar 

  74. Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in neural information processing systems. Barcelona, Spain; 2016. p. 3844–52.

  75. Stanković L, Daković M, Sejdić E. Introduction to graph signal processing. In: Stanković L, Sejdić E, editors. Vertex-Frequency Analysis of Graph Signals. Cham: Springer International Publishing; 2019. p. 3–108.

    Chapter  MATH  Google Scholar 

  76. Asgarian E, Kahani M, Sharifi S. HesNegar: Persian Sentiment WordNet. Signal Data Process. 2018;15(1):71–86.

    Article  Google Scholar 

  77. Mudinas A, Zhang D, Levene M. Combining lexicon and learning based approaches for concept-level sentiment analysis. In: Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining. Beijing, China; 2012. p. 1–8.

  78. Chen D, Lin Y, Li W, Li P, Zhou J, Sun X. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In: Proceedings of the AAAI Conference on Artificial Intelligence. New York, USA; 2020. p. 3438–45.

  79. Shuman D, Narang S, Frossard P, Ortega A, Vandergheynst P. The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process Mag. 2013;30(3):83–98.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering and Information Technology Department, University of Qom, Qom, Iran

    Abdalsamad Keramatfar, Hossein Amirkhani & Amir Jalaly Bidgoly

Authors
  1. Abdalsamad Keramatfar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hossein Amirkhani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amir Jalaly Bidgoly
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdalsamad Keramatfar.

Ethics declarations

Ethics Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Keramatfar, A., Amirkhani, H. & Bidgoly, A.J. Modeling Tweet Dependencies with Graph Convolutional Networks for Sentiment Analysis. Cogn Comput 14, 2234–2245 (2022). https://doi.org/10.1007/s12559-021-09986-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-021-09986-8

Keywords

Search

Navigation