Abstract
With explosive development of the World Wide Web, an enormous amount of text information containing users’ feeling, emotions and opinions has been generated and is increasingly employed by individuals and companies for making decisions. Whereas unstructured form of data must be analyzed to extract and summarize the opinions in them, sentiment analysis has changed to a significant research area in the field of Natural Language Processing. In this regard, deep learning methods have attracted a lot of attentions in recent years and various deep learning models have been proven as effective network architectures for the task of sentiment analysis. However, each of them has its potentials and weak points. To eliminate their drawbacks and make optimal use of their benefits, convolutional and recursive neural network are merged into a new robust model in this paper. The proposed model employs recursive neural network due to its tree structure as a substitute of pooling layer in the convolutional network with the aim of capturing long-term dependencies and reducing the loss of local information. The proposed model is validated on Stanford Sentiment Treebank by conducting a series of experiments and empirical results revealed that our model outperforms basic convolutional and recursive neural networks while requires fewer parameters.
Similar content being viewed by others
References
Cambria E, Poria S, Gelbukh A, Thelwall M (2017) Sentiment analysis is a big suitcase. IEEE Intell Syst 32(6):74–80
Vyas V, Uma V (2019) Approaches to sentiment analysis on product reviews. In: Sentiment analysis and knowledge discovery in contemporary business. IGI Global, pp 15–30
Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl-Based Syst 89:14–46
Souma W, Vodenska I, Aoyama H (2019) Enhanced news sentiment analysis using deep learning methods. J Comput Soc Sci 2:1–14
Ain QT, Ali M, Riaz A, Noureen A, Kamran M, Hayat B, Rehman A (2017) Sentiment analysis using deep learning techniques: a review. Int J Adv Comput Sci Appl 8(6):424
Sadr H, Nazari Solimandarabi M (2019) Presentation of an efficient automatic short answer grading model based on combination of pseudo relevance feedback and semantic relatedness measures. J Adv Comput Res 10(2):1–10
Jadidinejad A, Sadr H (2015) Improving weak queries using local cluster analysis as a preliminary framework. Indian J Sci Technol 8:15
Abirami A, Gayathri V (2017) A survey on sentiment analysis methods and approach. In: 8th International conference on advanced computing (ICoAC), IEEE, pp 72–76
Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113
Soleimandarabi MN, Mirroshandel SA (2015) A novel approach for computing semantic relatedness of geographic terms. Indian J Sci Technol 8:27
Soleimandarabi MN, Mirroshandel SA, Sadr H (2015) A Survey of semantic relatedness measures. Int J Comput Sci Network Solut 3(2):243–247
Young T, Hazarika D, Poria S, Cambria E (2018) Recent trends in deep learning based natural language processing. IEEE Comput Intell Mag 13(3):55–75
Mohammad SM (2017) Challenges in sentiment analysis. In: A practical guide to sentiment analysis. Springer, pp 61–83
Ouyang X, Zhou P, Li CH, Liu L (2015) Sentiment analysis using convolutional neural network. In: 2015 IEEE International conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing. IEEE, pp 2359–2364
Hassan A, Mahmood A (2017) Deep learning approach for sentiment analysis of short texts. In: 3rd International conference on control, automation and robotics (ICCAR), IEEE, pp 705–710
Van VD, Thai T, Nghiem M-Q (2017) Combining convolution and recursive neural networks for sentiment analysis. In: Proceedings of the 8th international symposium on information and communication technology, 2017. ACM, pp 151–158
Chen D, Zhang Y (2018) Robust zeroing neural-dynamics and its time-varying disturbances suppression model applied to mobile robot manipulators. IEEE Trans Neural Netw Learn Syst 29(9):4385–4397
Chen K, Yi C (2016) Robustness analysis of a hybrid of recursive neural dynamics for online matrix inversion. Appl Math Comput 273:969–975
Jin L, Li S, Hu B (2018) RNN models for dynamic matrix inversion: a control-theoretical perspective. IEEE Trans Ind Inf 14(1):189–199
You Q, Cao L, Jin H, Luo J (2016) Robust visual-textual sentiment analysis: When attention meets tree-structured recursive neural networks. In: Proceedings of the 24th ACM international conference on multimedia, ACM, pp 1008–1017
Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp 1188–1196
Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies-volume 1, Association for Computational Linguistics, pp 142–150
Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119
Islam J, Zhang Y (2016) Visual sentiment analysis for social images using transfer learning approach. In: 2016 IEEE international conferences on big data and cloud computing (BDCloud), social computing and networking (SocialCom), sustainable computing and communications (SustainCom)(BDCloud-SocialCom-SustainCom), IEEE, pp 124–130
Kim Y (2014) Convolutional neural networks for sentence classification. arXiv:14085882
Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification. In: Advances in neural information processing systems, pp 649-657
Socher R, Lin CC, Manning C, Ng AY (2011) Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 129–136
Socher R, Huval B, Manning CD, Ng AY (2012) Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, association for computational linguistics, pp 1201–1211
Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng A, 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
Wang X, Jiang W, Luo Z (2016) Combination of convolutional and recurrent neural network for sentiment analysis of short texts. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, pp 2428–2437
Huang Q, Chen R, Zheng X, Dong Z (2017) Deep sentiment representation based on CNN and LSTM. In: 2017 International conference on green informatics (ICGI), IEEE, pp 30–33
Timmaraju A, Khanna V (2015) Sentiment analysis on movie reviews using recursive and recurrent neural network architectures. Semantic Scholar
Jin L, Li S, Hu B, Liu M, Yu J (2019) A noise-suppressing neural algorithm for solving the time-varying system of linear equations: a control-based approach. IEEE Trans Ind Inf 15(1):236–246
Chen D, Li S, Wu Q (2019) Rejecting chaotic disturbances using a super-exponential-zeroing neurodynamic approach for synchronization of chaotic sensor systems. Sensors 19(1):74
Chen D, Zhang Y, Li S (2018) Tracking control of robot manipulators with unknown models: a Jacobian-matrix-adaption method. IEEE Trans Ind Inf 14(7):3044–3053
Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. arXiv:14042188
Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493–2537
Uličný M, Lundström J, Byttner S (2016) Robustness of deep convolutional neural networks for image recognition. In: International symposium on intelligent computing systems. Springer, pp 16–30
Park S, Kwak N (2016) Analysis on the dropout effect in convolutional neural networks. In: Asian conference on computer vision. Springer, pp 189–204
Du C, Huang L (2017) Sentiment classification via recurrent convolutional neural networks. DEStech Trans Comput Scie Eng (cii)
Yin W, Schütze H (2016) Multichannel variable-size convolution for sentence classification. arXiv:160304513
Tai KS, Socher R, Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. arXiv:150300075
Kokkinos F, Potamianos A (2017) Structural attention neural networks for improved sentiment analysis. arXiv:170101811
Wang Y, Huang M, Zhao L (2016) Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 606-615
Sharif M, Abadi HK (2018) Recursive nested neural network for sentiment analysis. Stanford University Report
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sadr, H., Pedram, M.M. & Teshnehlab, M. A Robust Sentiment Analysis Method Based on Sequential Combination of Convolutional and Recursive Neural Networks. Neural Process Lett 50, 2745–2761 (2019). https://doi.org/10.1007/s11063-019-10049-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-019-10049-1