Top

The Journal of Supercomputing

Published in:

17-01-2023

LSTM-SN: complex text classifying with LSTM fusion social network

Authors: Wei Wei, Xiaowan Li, Beibei Zhang, Linfeng Li, Robertas Damaševičius, Rafal Scherer

Published in: The Journal of Supercomputing | Issue 9/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Whether it is an NLP (natural language processing) task or an NLU (natural language understanding) task, many methods are model oriented, ignoring the importance of data features. Such models did not perform well for many tasks based on feature loose, unbalanced tricky data including text classification tasks. In this regard, this paper proposes a classification method called LSTM-SN (long-short term memory RNN fusion social network) based on extremely complex datasets. The approach condenses the characteristics of the dataset. LSTM combines with social network methods derived from specific datasets to complete the classification task, and then use complex network structure evolution methods to discover dynamic social attributes. The experimental results show that this method can overcome the shortcomings of traditional methods and achieve better classification results. Finally, a method to calculate the accuracy of fusion model is proposed. The research ideas of this paper have far-reaching significance in the domain of social data analysis and relation extraction.

previous article Improving drug discovery through parallelism

next article Dynamic decision-making strategy of replica number based on data hot

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Jie Z, Jianwen Z (2019) Machine learning classification problem and algorithm research. Software 40(7):205–208

Hang L (2019) Statistical learning methods. Tsinghua University Press. 567–78

Zhou B, Polap D, Wozniak M (2019) A regional adaptive variational PDE model for computed tomography image reconstruction. Pattern Recognit 92:64–81. https://doi.org/10.1016/j.patcog.2019.03.009CrossRef

Qiao K, Nowak J, Korytkowski M, Scherer R, Woniak M (2020) accurate and fast URL phishing detector: a convolutional neural network approach. Comput Netw 178(4):107275. https://doi.org/10.1016/j.comnet.2019.04.017CrossRef

Xia X, Marcin W, Fan X, Damasevicius R, Li Y (2019) Multi-sink distributed power control algorithm for cyber-physical-systems in coal mine tunnels. Comput Netw 161:210–219. https://doi.org/10.1016/j.comnet.2019.04.017CrossRef

Wei W, Song H, Wei L, Shen P, Vasilakos A (2017) Gradient-driven parking navigation using a continuous information potential field based on wireless sensor network. Inf Sci 408

Wang B, Shen T, Long G, et al (2021) Eliminating sentiment bias for aspect-level sentiment classification with unsupervised opinion extraction

Cherif W, Madani A, Kissi M (2021) Text categorization based on a new classification by thresholds. Progress Artif Intell 10(4):433–447CrossRef

Maldonado S, Vairetti C (2021) Efficient n-gram construction for text categorization using feature selection techniques. Intell Data Anal 25(3):509–525CrossRef

10.

Liu M, Liu L, Cao J, Du Q (2022) Co-attention network with label embedding for text classification. Neurocomputing 471:61–69CrossRef

11.

Ma Y, Liu X, Zhao L, Liang Y, Jin B (2021) Hybrid embedding-based text representation for hierarchical multi-label text classification. Expert Syst Appl 187(15):115905

12.

Kim Y (2014) Convolutional neural networks for sentence classification. In: EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 1746–1751). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/d14-1181

13.

Johnson R, Tong Z (2017) Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

14.

Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. Eprint Arxiv

15.

Zhuang T, Zhishu W (2021) Transformer-capsule integrated model for text classification. Comput Eng Appl 151–156

16.

Rajagopal D, Balachandran V, Hovy E, Tsvetkov Y (2021) SelfExplain: a self-explaining architecture for neural text classifiers. https://doi.org/10.48550/arXiv.2103.12279

17.

Wang H, Shi J, Zhang Z (2018) Semantic relation extraction of lstm based on attention mechanism. Comput Appl Res 35(5):143–146

18.

Peng Y, Xiao T, Yuan H (2021) Cooperative gating network based on a single BERT encoder for aspect term sentiment analysis. Appl Intell 52(5):5867–5879CrossRef

19.

Ramaswamy SL, Chinnappan J (2022) Recog Net-LSTM+CNN: a hybrid network with attention mechanism for aspect categorization and sentiment classification. J Intell Inform Syst 58(2):379–404CrossRef

20.

Sharma V, Srivastava S, Valarmathi B (2021) A comparative study on the performance of deep learning algorithms for detecting the sentiments expressed in modern slangs

21.

Guo H, Chi C and Zhan X, (2021) ERNIE-BiLSTM Based Chinese text sentiment classification method ICCEA 84–88.

22.

Peter S, Uszkoreit J, and Vaswani A (2018) Self-attention with relative position representations. arXiv preprint arXiv:1803.02155

23.

Shen T, Zhou T, Long G, Jiang J, Pan S, and Zhang C, Disan (2017) Directional self-attention network for rnn/cnn-free language understanding. arXiv preprint arXiv:1709.04696

24.

Moreno Y, Gómez JB, Pacheco AF (2003) Epidemic incidence in correlated complex networks. Phys Rev E 68:035103(R)CrossRef

25.

Naderipour M, Fazel Zarandi MH, Bastani S (2021) Fuzzy community detection on the basis of similarities in structural/attribute in large-scale social networks. Artif Intell Rev 55(2):1373–1407CrossRef

26.

Meng D, Sun L, Tian G (2022) Dynamic mechanism design on social networks. Games Econ Behav 131:84–120CrossRefMATHMathSciNet

27.

Sims, M, and D. Bamman. (2020) Measuring information propagation in literary social networks

28.

Wu Q, et al (2019) Dual graph aention networks for deep latent representation of multifaceted social e ects in recommender systems

29.

Roller S, ERK K, and Boleda G (2014) Inclusive yet selective: supervised distributional hypernymy detection. In COLING

30.

Nakashole N, Weikum G, and Suchanek F Pa & y: a taxonomy of relational Pa & erns with semantic types. In EMNLP

31.

Mikolov T, Sutskever I, Chen K, Corrado GS, and Dean J (2013) Distributed representations of words and phrases and their compositionality. In NIPS

32.

Aggarwal M, and Murty MN (2021) Machine learning in social networks: embedding nodes, edges, Communities, and Graphs

33.

Zhang B, Zhou Y, Xu X, Wang D, Guan X (2016) Dynamic structure evolution of time-dependent network. Physica A Stat Mech Appl 456:347–358CrossRef

34.

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, & Gomez AN et al. (2017) Attention is all you need. arXiv

35.

Huang K, Wang X (2022) ADA-INCVAE: Improved data generation using variational autoencoder for imbalanced classification. Appl Intell 1-16

36.

Qu, M, Ren X, & Han J (2017) Automatic synonym discovery with knowledge bases. ACM

37.

Szegedy C, Ioffe S, Vanhoucke V, et al (2017) Inception-v4, inception-res net and the impact of residual connections on learning

38.

Alshubaily I (2021) TextCNN with attention for text classification https://doi.org/10.48550/arXiv.2108.01921[P]

39.

Zhang Y, Wallace B (2015) A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification. Comput Sci

40.

Zhang Hu, Wang X, Hongye Ru, Tan L (2019) Applying data discretization to DPCNN for law article prediction. NLPCC 1:459–470

Title: LSTM-SN: complex text classifying with LSTM fusion social network
Authors: Wei Wei
Xiaowan Li
Beibei Zhang
Linfeng Li
Robertas Damaševičius
Rafal Scherer
Publication date: 17-01-2023
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 9/2023
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-022-05034-w

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 9/2023

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

High-performance fuzzy optimized deep convolutional neural network model for big data classification based on the social internet of things

Performance–energy trade-offs of deep learning convolution algorithms on ARM processors

TDMBBO: a novel three-dimensional migration model of biogeography-based optimization (case study: facility planning and benchmark problems)

BALANCER: bandwidth allocation and cache partitioning for multicore processors

Early detection and mitigation of TCP SYN flood attacks in SDN using chi-square test

Premium Partner