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Research on Sentiment Analysis of Network Forum Based on BP Neural Network

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Abstract

Nowadays, people pay more and more emotional to the emotional analysis of specific goals. Due to the long training time of many networks, this paper proposes a neural network with specific Objective sentiment analysis. Compared with the current neural network, the algorithm proposed in this paper has a shorter training time, which can effectively make up for the lack of emotional mechanism. Finally, we use the emotional data set to carry out simulation experiments. The experimental results show that the proposed algorithm is better than the ordinary neural network algorithm.

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References

  1. Abid F, Alam M, Yasir M, Li C (2019) Sentiment analysis through recurrent variants latterly on the convolutional neural network of twitter. Futur Gener Comput Syst 95:292–308

    Article  Google Scholar 

  2. Hu M, Yang C, Zheng Y, Wang X, He L, Ren F (2019) Facial expression recognition is based on fusion features of center-symmetric local signal magnitude pattern. IEEE Access 7:118435–118445

    Article  Google Scholar 

  3. Zheng WQ, Yu JS, Zou YX (2015) An experimental study of speech emotion recognition based on deep convolutional neural networks. In 2015 international conference on affective computing and intelligent interaction (ACII) (pp. 827-831). IEEE

  4. Xu G, Li W, Liu J (2020) A social emotion classification approach using multi-model fusion. Futur Gener Comput Syst 102:347–356

    Article  Google Scholar 

  5. Sun X, Li C, Ren F (2016) Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features. Neurocomputing 210:227–236

    Article  Google Scholar 

  6. Kim HR, Kim YS, Kim SJ, Lee IK (2018) Building emotional machines: recognizing image emotions through deep neural networks. IEEE Transactions on Multimedia 20(11):2980–2992

    Article  Google Scholar 

  7. Yang FE, Chang JC, Tsai CC, Wang YCF (2019) A multi-domain and multi-modal representation Disentangler for cross-domain image manipulation and classification. IEEE Trans Image Process 29:2795–2807

    Article  Google Scholar 

  8. Xiao Y, Lu M, Fu Z (2020) Covered face recognition based on deep convolution generative adversarial networks. In International Conference on Artificial Intelligence and Security (pp. 133-141). Springer, Cham

  9. Jiang M, Liang Y, Feng X, Fan X, Pei Z, Xue Y, Guan R (2018) Text classification based on deep belief network and softmax regression. Neural Comput & Applic 29(1):61–70

    Article  Google Scholar 

  10. Wei K, He B, Zhang T, He W (2008) Image emotional classification is based on the color semantic description. International Conference on Advanced Data Mining and Applications (pp. 485-491). Springer, Berlin, Heidelberg

  11. Mu R (2018) A survey of recommender systems based on deep learning. IEEE Access 6:69009–69022

    Article  Google Scholar 

  12. Zhang Y, Liu Y, Weninger F, Schuller B (2017) Multi-task deep neural network with shared hidden layers: breaking down the wall between emotion representations. In the 2017 IEEE international conference on acoustics, speech, and signal processing (ICASSP) (pp. 4990-4994). IEEE

  13. Lalwani S, Sharma H, Verma A, Kumar R (2019) Efficient discrete firefly algorithm for Ctrie based caching of multiple sequence alignment on optimally scheduled parallel machines. CAAI Transactions on Intelligence Technology 4(2):92–100

    Article  Google Scholar 

  14. Cheng B, Wang Z, Zhang Z, Li Z, Liu D, Yang J, Huang S, Huang TS (2017) Robust emotion recognition from low quality and low bit rate video: a deep learning approach. In 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 65-70). IEEE

  15. Nariman NA, Mohammad II, Karampour P (2019) Investigation of staggered block shear failure in a steel tension member utilizing minimax optimization. International Journal of Hydromechatronics 2(4):133–143

    Article  Google Scholar 

  16. Liang B, Quan L, Xu J, Qian Z, Peng Z (2017) Emotion analysis of specific goals based on multi emotional convolution neural network. Computer research and development 54(08):1724–1735

    Google Scholar 

  17. Mostafa SM (2019) Imputing missing values using cumulative linear regression. CAAI Transactions on Intelligence Technology 4(3):182–200

    Article  Google Scholar 

  18. Nakata M, Sakai H, Hara K (2019) Rule induction is based on rough sets from information tables having continuous domains. CAAI Transactions on Intelligence Technology 4(4):237–244

    Article  Google Scholar 

  19. Ouzaa K, Oucif C (2019) Creep effects on crack initiation and propagation in reinforced concrete walls. International Journal of Hydromechatronics 2(4):159–177

    Article  Google Scholar 

  20. Gao M, Dong J, Zhou D, Wei X, Zhang Q (2019) Speech emotion recognition based on convolutional neural network and feature fusion. In 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) (pp. 1145-1150). IEEE

  21. Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(140):1–67

    MathSciNet  MATH  Google Scholar 

  22. Ghaitani MM (2019) Dynamic displacement calculation in beams structures considering the effect of nanoparticles as the reinforcement phase. International Journal of Hydromechatronics 2(3):237–246

    Article  Google Scholar 

  23. Khater AA, El-Nagar AM, El-Bardini M, El-Rabie NM (2020) Online learning is based on adaptive learning rate for a class of recurrent fuzzy neural network. Neural Comput & Applic 32(12):8691–8710

    Article  Google Scholar 

Download references

Acknowledgments

The paper is supported by Guangxi Higher Education Undergraduate Teaching Reform Project Fund (2017JGA283) .

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Correspondence to Jianhuan Su.

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Tang, Y., Su, J. & Khan, M.A. Research on Sentiment Analysis of Network Forum Based on BP Neural Network. Mobile Netw Appl 26, 174–183 (2021). https://doi.org/10.1007/s11036-020-01697-y

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