Skip to main content
SpringerLink
Log in

Enhancing microblog sentiment analysis through multi-level feature interaction fusion with social relationship guidance

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

In sentiment analysis of microblogs that involve social relationships, a common approach is to expand the features of target microblogs using microblog relationship networks. However, the current research methodology only relies on individual interaction behaviors on social platforms to construct these networks, disregarding the guiding influence of microblog relationship networks on microblogs. Consequently, this leads to the feature expansion of microblogs while introducing interference among them. To address this problem, this study aims to construct a more precise microblog relationship network by incorporating multiple interactive behaviors from social platforms. This network will serve as a guide for sentiment interactions among microblog texts, thereby mitigating feature interference. Firstly, we utilize various interaction behaviors on social platforms to build a microblog relationship network. We employ a LINE network embedding to represent the microblog relationship network as microblog relationship features. Secondly, we extract word-level and sentence-level features from the microblog text using a BERT pre-training model. The word-level features are combined using convolutional neural networks. Subsequently, the word-level and sentence-level features of microblogs are separately guided for interaction fusion through relational features. An attention network is then constructed to fuse the post-interaction features in a single step. Prior to secondary fusion, the primary fusion features, post-interaction word-level features, and post-interaction sentence-level features are weighted, and the sentiment categories of microblogs are outputted. Finally, we compare the proposed method with the text-only microblog sentiment analysis approach and the sentiment analysis method that incorporates social relationships on two real datasets. The comparison results demonstrate the superiority of our proposed method.

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
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

All the datasets are publicly available and can be obtained using the cited sources.

References

  1. Asha V, Vishwanatha C, Kumar A, Shilpa S, Shivaiah G, Kumar A (2023) Sentimental analysis of lockdown in india during covid-19. In: 2023 International conference on applied intelligence and sustainable computing (ICAISC), pp 1–6. IEEE

  2. Chen Y (2015) Convolutional neural network for sentence classification. Master’s thesis, University of Waterloo

  3. Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805

  4. Fan T, Wang H, Wu P, Ling C, Ahvanooey MT (2023) Multimodal sentiment analysis for social media contents during public emergencies. J Data Inf Sci

  5. Gan C, Cao X, Zhu Q (2023) Microblog sentiment analysis via user representative relationship under multi-interaction hybrid neural networks. Multimedia Systems pp 1–12

  6. Gupta P, Kumar S, Suman RR, Kumar V (2020) Sentiment analysis of lockdown in india during covid-19: a case study on twitter. IEEE Trans Comput Soc Syst 8(4):992–1002

    Article  Google Scholar 

  7. Hu X, Tang L, Tang J, Liu H (2013) Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the sixth ACM international conference on Web search and data mining, pp 537–546

  8. Jia K (2022) Sentiment classification of microblog: a framework based on bert and cnn with attention mechanism. Comput Electr Eng 101:108,032

    Article  Google Scholar 

  9. Joulin A, Grave E, Bojanowski P, Mikolov T (2016) Bag of tricks for efficient text classification. arXiv:1607.01759

  10. Keramatfar A, Amirkhani H, Bidgoly AJ (2022) Modeling tweet dependencies with graph convolutional networks for sentiment analysis. Cognit Comput 14(6):2234–2245

    Article  Google Scholar 

  11. Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of the 19th international conference on World wide web, pp 591–600

  12. Li X, Zhang J, Du Y, Zhu J, Fan Y, Chen X (2023) A novel deep learning-based sentiment analysis method enhanced with emojis in microblog social networks. Enterp Inf Syst 17(5):2037,160

    Article  Google Scholar 

  13. Li YJ, Zhang HJ, Pan WM, Feng RJ, Zhou ZY (2021) Microblog rumor detection based on bert-dpcnn. In: Artificial Intelligence in China: proceedings of the 2nd international conference on artificial intelligence in China, pp 524–530. Springer

  14. Liu W, Zhang M (2019) Semi-supervised sentiment classification method based on weibo social relationship. In: Web information systems and applications: 16th international conference, WISA 2019, Qingdao, China, September 20-22, 2019, Proceedings 16, pp 480–491. Springer

  15. Mi C, Ruan X, Xiao L (2022) Microblog sentiment analysis using user similarity and interaction-based social relations. In: Research anthology on implementing sentiment analysis across multiple disciplines, pp 1887–1904. IGI Global

  16. Pozzi FA, Maccagnola D, Fersini E, Messina E (2013) Enhance user-level sentiment analysis on microblogs with approval relations. In: AI* IA 2013: Advances in artificial intelligence: XIIIth international conference of the italian association for artificial intelligence, Turin, Italy, December 4-6, 2013. Proceedings 13, pp 133–144. Springer

  17. Rahman MM, Islam MN (2021) Exploring the performance of ensemble machine earning classifiers for sentiment analysis of covid-19 tweets. In: Sentimental analysis and deep learning: proceedings of ICSADL 2021, pp 383–396. Springer

  18. Saif H, He Y, Fernandez M, Alani H (2016) Contextual semantics for sentiment analysis of twitter. Inf Process Manage 52(1):5–19

    Article  Google Scholar 

  19. Shamma DA, Kennedy L, Churchill EF (2009) Tweet the debates: understanding community annotation of uncollected sources. In: Proceedings of the first SIGMM workshop on Social media, pp 3–10

  20. Speriosu M, Sudan N, Upadhyay S, Baldridge J (2011) Twitter polarity classification with label propagation over lexical links and the follower graph. In: Proceedings of the first workshop on unsupervised learning in NLP, pp 53–63

  21. Sun B, Tian F, Liang L (2018) Tibetan micro-blog sentiment analysis based on mixed deep learning. In: 2018 international conference on audio, language and image processing (ICALIP), pp 109–112. IEEE

  22. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web, pp 1067–1077

  23. Tao Y, Zhang X, Shi L, Wei L, Hai Z, Wahid JA (2019) Joint embedding of emoticons and labels based on cnn for microblog sentiment analysis. In: 2019 IEEE Fourth international conference on data science in cyberspace (DSC), pp 168–175. IEEE

  24. Wang M, Hu G (2020) A novel method for twitter sentiment analysis based on attentional-graph neural network. Information 11(2):92

    Article  Google Scholar 

  25. Wang S, Zhu Y, Gao W, Cao M, Li M (2020) Emotion-semantic-enhanced bidirectional lstm with multi-head attention mechanism for microblog sentiment analysis. Information 11(5):280

    Article  Google Scholar 

  26. Wei J, Liao J, Yang Z, Wang S, Zhao Q (2020) Bilstm with multi-polarity orthogonal attention for implicit sentiment analysis. Neurocomputing 383:165–173

    Article  Google Scholar 

  27. Wu F, Huang Y, Song Y (2016) Structured microblog sentiment classification via social context regularization. Neurocomputing 175:599–609

    Article  Google Scholar 

  28. Wu J, Lu K, Su S, Wang S (2019) Chinese micro-blog sentiment analysis based on multiple sentiment dictionaries and semantic rule sets. IEEE Access 7:183,924-183,939. https://doi.org/10.1109/ACCESS.2019.2960655

    Article  Google Scholar 

  29. Xu J, Huang F, Zhang X, Wang S, Li C, Li Z, He Y (2019) Sentiment analysis of social images via hierarchical deep fusion of content and links. Appl Soft Comput 80:387–399

  30. Yang J, Zou X, Zhang W, Han H (2021) Microblog sentiment analysis via embedding social contexts into an attentive lstm. Eng Appl Artif Intell 97:104,048

    Article  Google Scholar 

  31. Zhang Q, Zhang Z, Yang M, Zhu L (2021) Exploring coevolution of emotional contagion and behavior for microblog sentiment analysis: a deep learning architecture. Complexity 2021:1–10

    Google Scholar 

  32. Zhang Z, Han X, Liu Z, Jiang X, Sun M, Liu Q (2019) Ernie: enhanced language representation with informative entities. arXiv:1905.07129

  33. Zhao K, Zhang Y, Zhang Y, Xing C, Li C (2019) Learning from user social relation for document sentiment classification. In: Database systems for advanced applications: 24th international conference, DASFAA 2019, Chiang Mai, Thailand, April 22–25, 2019, Proceedings, Part II 24, pp 86–103. Springer

  34. Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, Xu B (2016) Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th annual meeting of the association for computational linguistics (vol 2: Short papers), pp 207–212

  35. Zou X, Yang J, Zhang J (2018) Microblog sentiment analysis using social and topic context. PloS one 13(2):e0191,163

    Article  Google Scholar 

  36. Zou X, Yang J, Zhang W, Han H (2021) Collaborative community-specific microblog sentiment analysis via multi-task learning. Expert Syst Appl 169:114,322

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 61903056 and 61702066) and the Chongqing Research Program of Basic Research and Frontier Technology (Nos. cstc2019jcyj-msxmX0681 and cstc2021jcyj-msxmX0761). This work was partially supported by Project PID2020-119478GB-I00 funded by MICINN/AEI/10.13039/501100011033.

Author information

Authors and Affiliations

  1. School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

    Chenquan Gan & Qingyi Zhu

  2. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

    Xiaopeng Cao

  3. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, 116024, Dalian, China

    Deepak Kumar Jain

  4. Symbiosis Institute of Technology, Symbiosis International University, 412115, Pune, India

    Deepak Kumar Jain

  5. Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071, Granada, Spain

    Salvador García

Authors
  1. Chenquan Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaopeng Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qingyi Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Deepak Kumar Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Salvador García
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, Methodology, Formal analysis and investigation, Writing - original draft preparation: Chenquan Gan, Xiaopeng Cao; Writing - review and editing: Qingyi Zhu; Writing - review and editing, Supervision: Deepak Kumar Jain and Salvador García.

Corresponding author

Correspondence to Salvador García.

Ethics declarations

Conflicts of interest

The authors have no competing interests.

Ethical and informed consent for data used

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

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gan, C., Cao, X., Zhu, Q. et al. Enhancing microblog sentiment analysis through multi-level feature interaction fusion with social relationship guidance. Appl Intell 54, 443–459 (2024). https://doi.org/10.1007/s10489-023-05206-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-05206-y

Keywords

Search

Navigation