Nothing Special   »   [go: up one dir, main page]
Skip to main content
SpringerLink
Log in

Assessing a BERT-based model for analyzing subjectivity and classifying academic articles

  • 1247: Recent Advances in AI-Powered Multimedia Visual Computing and Multimodal Signal Processing for Metaverse Era
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The metaverse concept extends beyond virtual worlds and can be applied to collaborative analysis environments. Data analysts worldwide may read academic article extracts in real-time in a shared digital workplace to mine and analyze data using the metaverse. Furthermore, a semantic metaverse in natural language processing might also involve creating a digital environment with linguistic and semantic connections. Many dedicated researchers navigating the complexities of natural language processing in the metaverse era have spent considerable time searching for relevant papers. However, online reviews and evaluations of articles are helpful for their assistance and may save the researcher time. In this work, human specialists manually produced a dataset from four conferences and evaluated subjectively using rule-based techniques. Subsequently, we aim to evaluate the effectiveness of pre-trained word embeddings and pre-trained BERT models seamlessly integrated with convolutional neural networks. This endeavor focuses on the subjective analysis and classification of contributions and previous work sentences extracted from academic literature. For comparison, various deep learning architectures were systematically employed, including long short-term memory-GloVe and bi-directional long short-term memory-GloVe, alongside classical machine learning methods. Our findings show that the proposed BERT model achieved state-of-the-art performance in classification and subjective analysis tasks with an accuracy of 91.50% and F1 score of 91.00%. Finally, we plan to utilize sentence similarity to identify contributions within abstracts, thus outlining potential avenues for future research.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The data supporting the findings of this study are available upon reasonable request. Please get in touch with the corresponding author for access to the data.

References

  1. Dehkharghani R, Saygin Y, Yanikoglu B, Oflazer K (2016) SentiTurkNet: a Turkish polarity lexicon for sentiment analysis. Lang Resour Eval 50(3):667–685. https://doi.org/10.1007/s10579-015-9307-6

    Article  Google Scholar 

  2. Thangavel P, Lourdusamy R (2023) A lexicon-based approach for sentiment analysis of multimodal content in tweets. Multimed Tools Appl 82(16):24203–24226. https://doi.org/10.1007/s11042-023-14411-3

    Article  Google Scholar 

  3. Agarwal B, Mittal N, Bansal P, Garg S (2015) Sentiment analysis using common-sense and context information. Comput Intell Neurosci 2015:1–9. https://doi.org/10.1155/2015/715730

    Article  Google Scholar 

  4. Zhu X, Zhu Y, Zhang L, Chen Y (2023) A BERT-based multi-semantic learning model with aspect-aware enhancement for aspect polarity classification. Appl Intell 53(4):4609–4623. https://doi.org/10.1007/s10489-022-03702-1

    Article  Google Scholar 

  5. Gao Z, Feng A, Song X, Wu X (2019) Target-dependent sentiment classification with BERT. IEEE Access 7:154290–154299. https://doi.org/10.1109/ACCESS.2019.2946594

    Article  Google Scholar 

  6. Ullah A, Khan SN, Nawi NM (2023) Review on sentiment analysis for text classification techniques from 2010 to 2021. Multimed Tools Appl 82(6):8137–8193. https://doi.org/10.1007/s11042-022-14112-3

    Article  Google Scholar 

  7. Ittoo A, Nguyen LM, Van Den Bosch A (2016) Text analytics in industry: challenges, desiderata and trends. Comput Ind 78:96–107. https://doi.org/10.1016/j.compind.2015.12.001

    Article  Google Scholar 

  8. Ravi K, Ravi V (2015) Knowledge-based systems a survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-Based Syst 89:14–46. https://doi.org/10.1016/j.knosys.2015.06.015

    Article  Google Scholar 

  9. Nguyen D et al (2020) How we do things with words: analyzing text as social and cultural data. Front Artif Intell 3(August):1–14. https://doi.org/10.3389/frai.2020.00062

    Article  Google Scholar 

  10. Bhavitha BK, Rodrigues AP, Chiplunkar NN (2017) Comparative study of machine learning techniques in sentimental analysis. Proc Int Conf Inven Commun Comput Technol ICICCT 2017, no. Icicct, pp 216–221. https://doi.org/10.1109/ICICCT.2017.7975191.

  11. Lee SW, Jiang G, Kong HY, Liu C (2021) A difference of multimedia consumer’s rating and review through sentiment analysis. Multimed Tools Appl 80(26–27):34625–34642. https://doi.org/10.1007/s11042-020-08820-x

    Article  Google Scholar 

  12. Cai Y et al (2019) A hybrid model for opinion mining based on domain sentiment dictionary. Int J Mach Learn Cybern 10(8):2131–2142. https://doi.org/10.1007/s13042-017-0757-6

    Article  Google Scholar 

  13. Appel O, Chiclana F, Carter J, Fujita H (2016) A hybrid approach to the sentiment analysis problem at the sentence level. Knowledge-Based Syst 108:110–124. https://doi.org/10.1016/j.knosys.2016.05.040

    Article  Google Scholar 

  14. Zhu C, Yi B, Luo L (2024) Base on contextual phrases with cross-correlation attention for aspect-level sentiment analysis. Expert Syst Appl 241(September 2023):122683. https://doi.org/10.1016/j.eswa.2023.122683

    Article  Google Scholar 

  15. Saleh H, Alhothali A, Moria K (2023) Detection of hate speech using BERT and hate speech word embedding with deep model. Appl Artif Intell 37(1). https://doi.org/10.1080/08839514.2023.2166719

  16. Muhammad A, Wiratunga N, Lothian R (2016) Contextual sentiment analysis for social media genres. Knowledge-Based Syst 108:92–101. https://doi.org/10.1016/j.knosys.2016.05.032

    Article  Google Scholar 

  17. Fernández-Gavilanes M, Álvarez-López T, Juncal-Martínez J, Costa-Montenegro E, Javier González-Castaño F (2016) Unsupervised method for sentiment analysis in online texts. Expert Syst Appl 58:57–75. https://doi.org/10.1016/j.eswa.2016.03.031

    Article  Google Scholar 

  18. Araque O, Corcuera-Platas I, Sánchez-Rada JF, Iglesias CA (2017) Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst Appl 77:236–246. https://doi.org/10.1016/j.eswa.2017.02.002

    Article  Google Scholar 

  19. Severyn A, Moschitti A (2015) UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification. In: SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings, pp 464–469. https://doi.org/10.18653/v1/s15-2079

  20. Mewada A, Dewang RK (2023) SA-ASBA: a hybrid model for aspect-based sentiment analysis using synthetic attention in pre-trained language BERT model with extreme gradient boosting. J Supercomput 79(5):5516–5551. https://doi.org/10.1007/s11227-022-04881-x

    Article  Google Scholar 

  21. Bansal B, Srivastava S (2018) Sentiment classification of online consumer reviews using word vector representations. Procedia Comput Sci 132:1147–1153. https://doi.org/10.1016/j.procs.2018.05.029

    Article  Google Scholar 

  22. Tang D, Qin B, Liu T (2015) Document modeling with gated recurrent neural network for sentiment classification. Conf Proc - EMNLP 2015 Conf Empir Methods Nat Lang Process, no. September, pp 1422–1432. https://doi.org/10.18653/v1/d15-1167

  23. Peters ME, Neumann M, Zettlemoyer L, Yih WT (2018) Dissecting contextual word embeddings: Architecture and representation. Proc 2018 Conf Empir Methods Nat Lang Process EMNLP 2018, pp 1499–1509. https://doi.org/10.18653/v1/d18-1179

  24. Rao G, Huang W, Feng Z, Cong Q (2018) LSTM with sentence representations for document-level sentiment classification. Neurocomputing 308:49–57. https://doi.org/10.1016/j.neucom.2018.04.045

    Article  Google Scholar 

  25. Li W, Zhu L, Shi Y, Guo K, Cambria E (2020) User reviews: Sentiment analysis using lexicon integrated two-channel CNN–LSTM family models. Appl Soft Comput J 94:106435. https://doi.org/10.1016/j.asoc.2020.106435

    Article  Google Scholar 

  26. Giménez M, Palanca J, Botti V (2020) Semantic-based padding in convolutional neural networks for improving the performance in natural language processing. A case of study in sentiment analysis. Neurocomputing 378:315–323. https://doi.org/10.1016/j.neucom.2019.08.096

    Article  Google Scholar 

  27. Gupta K, Ahmad A, Ghosal T, Ekbal A (2024) A BERT-based sequential deep neural architecture to identify contribution statements and extract phrases for triplets from scientific publications. Int J Digit Libr. https://doi.org/10.1007/s00799-023-00393-y

    Article  Google Scholar 

  28. Jain PK, Quamer W, Saravanan V, Pamula R (2023) Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis. J Ambient Intell Humaniz Comput 14(8):10417–10429. https://doi.org/10.1007/s12652-022-03698-z

    Article  Google Scholar 

  29. Zhang X, Wu Z, Liu K, Zhao Z, Wang J, Wu C (2023) Text sentiment classification based on BERT embedding and sliced multi-head self-attention Bi-GRU. Sensors 23(3):1481. https://doi.org/10.3390/s23031481

    Article  Google Scholar 

  30. Liu S, Lee I (2021) Sequence encoding incorporated CNN model for Email document sentiment classification. Appl Soft Comput 102:107104. https://doi.org/10.1016/j.asoc.2021.107104

    Article  Google Scholar 

  31. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst 4(January):3104–3112

    Google Scholar 

  32. Jozefowicz R, Zaremba W, Sutskever I (2015) An empirical exploration of Recurrent Network architectures. 32nd Int Conf Mach Learn ICML 2015 3:2332–2340

    Google Scholar 

  33. Goularte FB, Martins BE da G, Carvalho PCQ da F, Won M (2024) SentPT: a customized solution for multi-genre sentiment analysis of Portuguese-language texts. Expert Syst Appl 245(22):123075. https://doi.org/10.1016/j.eswa.2023.123075

    Article  Google Scholar 

Download references

Acknowledgements

We want to extend our sincere gratitude to the following individuals for their valuable contributions to this article: Conceptualization; Methodology; Data Curation; Writing—original draft; Resources: Atif Mehmood Formal analysis and investigation; Resources; Writing—original draft preparation: Farah Shahid Formal analysis, language editing, and investigation; Writing—review and editing; Validation: Rizwan Khan; Mostafa M. Ibrahim Resources; Visualization: Shahzad Ahmed Funding acquisition; Visualization; Supervision: Zhonglong Zheng.

Funding

This work was funded by the National Natural Science Foundation of China (NSFC62272419), U22A20102, the Natural Science Foundation of Zhejiang Province (ZJNSFLZ22F020010), and the Zhejiang Normal University Research Fund (ZC304022915), and research work was partially funded by the Zhejiang Normal University research funds (YS304023947 and YS304023948).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321002, China

    Atif Mehmood, Farah Shahid, Rizwan Khan & Zhonglong Zheng

  2. Zhejiang Institute of Photoelectronics & Zhejiang Institute for Advanced Light Source, Zhejiang Normal University, Jinhua, Zhejiang, 321004, China

    Atif Mehmood & Farah Shahid

  3. Faculty of Information Technology, Beijing University of Technology, Beijing, 100024, China

    Shahzad Ahmed

  4. Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, 61519, Egypt

    Mostafa M. Ibrahim

Authors
  1. Atif Mehmood
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farah Shahid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rizwan Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shahzad Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mostafa M. Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhonglong Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization; Methodology; Data Curation; Writing—original draft; Resources: Atif Mehmood; Formal analysis and investigation; Resources; Writing—original draft preparation: Farah Shahid; Formal analysis and investigation; Writing—review and editing; Validation: Rizwan Khan; Mostafa M. Ibrahim; Resources; Visualization: Shahzad Ahmed; Funding acquisition; Visualization; Supervision: Zhonglong Zheng.

Corresponding author

Correspondence to Zhonglong Zheng.

Ethics declarations

Competing interest

We declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere.

Conflicts of interest

The authors declare no conflict of interest.

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

Mehmood, A., Shahid, F., Khan, R. et al. Assessing a BERT-based model for analyzing subjectivity and classifying academic articles. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19206-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-19206-8

Keywords

Search

Navigation