Sentiment Analysis Using Gated Recurrent Neural Networks | SN Computer Science Skip to main content
Springer Nature Link
Log in

Sentiment Analysis Using Gated Recurrent Neural Networks

  • Review Article
  • Published:
SN Computer Science Aims and scope Submit manuscript

A Publisher Correction to this article was published on 28 September 2023

This article has been updated

Abstract

Text sentiment analysis is an important and challenging task. Sentiment analysis of customer reviews is a common problem faced by companies. It is a machine learning problem made demanding due to the varying nature of sentences, different lengths of the paragraphs of text, contextual understanding, sentiment ambiguity and the use of sarcasm and comparatives. Traditional approaches to sentiment analysis use the tally or recurrence of words in a text which are allotted sentiment values by some expert. These strategies overlook the order of words and the complex different meanings they can communicate. Hence, RNNs were introduced that are effective yet challenging to train. Bi-GRUs and Bi-LSTM architectures are a recent form of RNNs which can store information about long-term dependencies in sequential data. In this work, we attempted a survey of different deep learning techniques that have been applied to sentiment classification and analysis. We have implemented the baseline models for LSTM, GRU and Bi-LSTM and Bi-GRU on an Amazon review dataset.

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
¥17,985 /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 includes VAT (Japan)

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

Explore related subjects

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

Change history

References

  1. Erevelles S, Fukawa N, Swayne L. Big Data consumer analytics and the transformation of marketing. J Bus Res. 2016;69:897–904.

    Article  Google Scholar 

  2. Rosenthal S, Farra N, Nakov P. SemEval-2017 task 4: sentiment analysis on Twitter. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017); 2017. p. 502–518.

  3. O’Connor B, Balasubramanyan R, Routledge BR, Smith NA. From tweets to polls: linking text sentiment to public opinion time series. In: Fourth international AAAI conference on weblogs and social media; 2010.

  4. Jozefowicz R, Zaremba W, Sutskever I. An empirical exploration of recurrent network architectures. In: International conference on machine learning; 2015. p. 2342–50.

  5. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.

    Article  Google Scholar 

  6. Cho K, Merrienboer BV, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing; 2014. p. 1724–1734.

  7. Ain QT, Ali M, Riaz A, Noureen A, Kamran M, Hayat B, Rehman A. Sentiment analysis using deep learning techniques: a review. Int J Adv Comput Sci Appl. 2017;8(6):424.

    Google Scholar 

  8. Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J. 2014;5(4):1093–113.

    Article  Google Scholar 

  9. Graves A, Liwicki M, Fernández S, Bertolami R, Bunke H, Schmidhuber J. A novel connectionist system for unconstrained handwriting recognition. IEEE Trans Pattern Anal Mach Intell. 2008;31(5):855–68.

    Article  Google Scholar 

  10. Graves A, Mohamed AR, Hinton G. Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing. IEEE; 2013. p. 6645–49.

  11. Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint. 2014; arXiv:1412.3555.

  12. Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint. 2015; arXiv:1508.01991.

  13. 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; 2016. p. 1480–9.

  14. Lakkaraju H, Socher R, Manning C. Aspect specific sentiment analysis using hierarchical deep learning. In: NIPS Workshop on deep learning and representation learning; 2014.

  15. Al-Smadi M, Talafha B, Al-Ayyoub M, Jararweh Y. Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. Int J Mach Learn Cybern. 2019;10(8):2163–75.

    Article  Google Scholar 

  16. Guo J, Cheng J, Cleland-Huang J. Semantically enhanced software traceability using deep learning techniques. In: IEEE/ACM 39th international conference on software engineering; 2017. p. 3–14.

  17. Rao G, Huang W, Feng Z, Cong Q. LSTM with sentence representations for document-level sentiment classification. Neurocomputing. 2018;308:49–57.

    Article  Google Scholar 

  18. Baktha K, Tripathy BK. Investigation of recurrent neural networks in the field of sentiment analysis. In: 2017 international conference on communication and signal processing (ICCSP). IEEE; 2017. p. 2047–50.

  19. Tutubalina E, Nikolenko S. Combination of deep recurrent neural networks and conditional random fields for extracting adverse drug reactions from user reviews. J Healthc Eng. 2017;1–9.

  20. Dey R, Salemt FM. Gate-variants of gated recurrent unit (GRU) neural networks. In: 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). IEEE; 2017. p. 1597–1600.

  21. Wöllmer M, Weninger F, Knaup T, Schuller B, Sun C, Sagae K, Morency LP. Youtube movie reviews: sentiment analysis in an audio-visual context. IEEE Intell Syst. 2013;28(3):46–53.

    Article  Google Scholar 

  22. Ruales J. Recurrent neural networks for sentiment analysis. In: IEEE. Colombia: Colombia University; 2011.

  23. Minh DL, Sadeghi-Niaraki A, Huy HD, Min K, Moon H. Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access. 2018;6:55392–404.

    Article  Google Scholar 

  24. Wang N, Wang J, Zhang X. YNU-HPCC at IJCNLP-2017 Task 4: attention-based Bi-directional GRU model for customer feedback analysis task of English. In: Proceedings of the IJCNLP; 2017. p. 174–9.

  25. Zhang L, Zhou Y, Duan X, Chen R. A hierarchical multi-input and output Bi-GRU Model for sentiment analysis on customer reviews. In: IOP conference series: materials science and engineering, vol. 322, no. 6; IOP Publishing; 2018. p. 062007.

  26. Huang Y, Jiang Y, Hasan T, Jiang Q, Li C. A topic Bi-LSTM model for sentiment classification. In: Proceedings of the 2nd international conference on innovation in artificial intelligence. ACM; 2018. p. 143–7.

  27. Wu J, Zheng K, Sun J. Text sentiment classification based on layered attention network. In: Proceedings of the 2019 3rd high-performance computing and cluster technologies conference. ACM; 2019. p. 162–6.

  28. Luo LX. Network text sentiment analysis method combining LDA text representation and GRU-CNN. Pers Ubiquit Comput. 2019;23(3–4):405–12.

    Article  Google Scholar 

  29. Majumder N, Hazarika D, Gelbukh A, Cambria E, Poria S. Multimodal sentiment analysis using hierarchical fusion with context modeling. Knowl-Based Syst. 2018;161:124–33.

    Article  Google Scholar 

  30. Jabreel M, Hassan F, Moreno A. Target-dependent sentiment analysis of tweets using bidirectional gated recurrent neural networks. In: Advances in hybridization of intelligent methods. Cham: Springer; 2018. p. 39–55.

  31. Shen T, Zhou T, Long G, Jiang J, Zhang C. Bi-directional block self-attention for fast and memory-efficient sequence modeling. arXiv preprint. 2018; arXiv:1804.00857.

  32. Piao G, Breslin JG. Financial aspect and sentiment predictions with deep neural networks: an ensemble approach. In: Companion Proceedings of the Web Conference 2018. International World Wide Web Conferences Steering Committee; 2018. p. 1973–7.

  33. Wang Y, Sun A, Han J, Liu Y, Zhu X. Sentiment analysis by capsules. In: Proceedings of the 2018 World Wide Web Conference. International World Wide Web Conferences Steering Committee; 2018. p. 1165–74.

  34. Penghua Z, Dingyi Z. Bidirectional-GRU based on attention mechanism for aspect-level sentiment analysis. In: Proceedings of the 2019 11th international conference on machine learning and computing. ACM; 2019. p. 86–90.

  35. Bjerva J, Plank B, Bos J. Semantic tagging with deep residual networks. CoRR J. 1609.07053. 2016.

  36. Zhou J, Huang JX, Chen Q, Hu QV, Wang T, He L. Deep learning for aspect-level sentiment classification: survey, vision and challenges. IEEE Access. 2019;7:78454–83.

    Article  Google Scholar 

  37. Gupte A, Joshi S, Gadgul P, Kadam A, Gupte A. Comparative study of classification algorithms used in sentiment analysis. Int J Comput Sci Inf Technol. 2014;5(5):6261–4.

    Google Scholar 

  38. Fang X, Zhan J. Sentiment analysis using product review data. J Big Data. 2015;2(1):5.

    Article  Google Scholar 

  39. Zeng D, Dai Y, Li F, Wang J, Sangaiah AK. Aspect based sentiment analysis by a linguistically regularized CNN with gated mechanism. J Intell Fuzzy Syst. 2019;36:3971–80.

    Article  Google Scholar 

  40. Lei Z, Yang Y, Yang M, Liu Y. A multi-sentiment-resource enhanced attention network for sentiment classification. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 2: Short Papers); 2018. p. 758–763.

  41. Li L, Liu Y, Zhou A. Hierarchical attention based position-aware network for aspect-level sentiment analysis. In: Proceedings of the 22nd conference on computational natural language learning; 2018. p. 181–9.

  42. Kumar A, Rastogi R. Attentional recurrent neural networks for sentence classification. In Innovations in infrastructure. Springer; 2019. pp. 549–59.

  43. Bagheri A, Saraee M, De Jong F. Care more about customers: unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowl-Based Syst. 2013;52:201–13.

    Article  Google Scholar 

  44. Khan A, Baharudin B, Lee LH, Khan K. A review of machine learning algorithms for text-documents classification. J Adv Inf Technol. 2010;1(1):4–20.

    Google Scholar 

  45. Prabowo R, Thelwall M. Sentiment analysis: a combined approach. J Informetr. 2009;3(2):143–57.

    Article  Google Scholar 

  46. Thet TT, Na JC, Khoo CS. Aspect-based sentiment analysis of movie reviews on discussion boards. J Inf Sci. 2010;36(6):823–48.

    Article  Google Scholar 

  47. Ni J, Li J, McAuley J. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th International joint conference on natural language processing. 2019; p. 188–197.

Download references

Author information

Authors and Affiliations

  1. Bharati Vidyapeeth’s College of Engineering, New Delhi, India

    Sharat Sachin, Abha Tripathi, Navya Mahajan, Shivani Aggarwal & Preeti Nagrath

Authors
  1. Sharat Sachin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abha Tripathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Navya Mahajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shivani Aggarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Preeti Nagrath
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Preeti Nagrath.

Additional information

Publisher's Note

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

This article is part of the topical collection “Deep learning approaches for data analysis: A practical perspective” guest edited by D. Jude Hemanth, Lipo Wang and Anastasia Angelopoulou.

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

Sachin, S., Tripathi, A., Mahajan, N. et al. Sentiment Analysis Using Gated Recurrent Neural Networks. SN COMPUT. SCI. 1, 74 (2020). https://doi.org/10.1007/s42979-020-0076-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-020-0076-y

Keywords

Search

Navigation