JACIII Vol.23 No.6 pp. 1044-1051
doi: 10.20965/jaciii.2019.p1044


Research on Multi-Channel Semantic Fusion Classification Model

Di Yang, Ningjia Qiu, Lin Cong, and Huamin Yang

School of Computer Science and Technology, Changchun University of Science and Technology
No.7186 Wei Xing Road, Chaoyang District, Changchun, Jilin 130022, China

Corresponding author

September 15, 2018
July 9, 2019
November 20, 2019
semantic fusion, emotional tendency weights, multi-channel, sentiment classification

In this work, we propose a multi-channel semantic fusion convolutional neural network (SFCNN) to solve the problem of emotional ambiguity caused by the change of contextual order in sentiment classification task. Firstly, the emotional tendency weights are evaluated on the text word vector through the improved emotional tendency attention mechanism. Secondly, the multi-channel semantic fusion layer is leveraged to combine deep semantic fusion of sentences with contextual order to generate deep semantic vectors, which are learned by CNN to extract high-level semantic features. Finally, the improved adaptive learning rate gradient descent algorithm is employed to optimize the model parameters, and completes the sentiment classification task. Three datasets are used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the SFCNN model has the high steady-state precision and generalization performance.

Cite this article as:
D. Yang, N. Qiu, L. Cong, and H. Yang, “Research on Multi-Channel Semantic Fusion Classification Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.6, pp. 1044-1051, 2019.
Data files:
  1. [1] B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Foundations and Trends in Information Retrieval, Vol.2, No.1-2, pp. 1-135, 2008.
  2. [2] M. Giatsoglou, M. G. Vozalis, K. Diamantaras, A. Vakali, G. Sarigiannidis, and K. C. Chatzisavvas, “Sentiment analysis leveraging emotions and word embeddings,” Expert Systems with Applications, Vol.69, pp. 214-224, 2017.
  3. [3] X. Glorot, A. Bordes, and Y. Bengio, “Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach,” Proc. of the 28th Int. Conf. on Machine Learning, pp. 513-520, 2011.
  4. [4] K. Han, W. Wan, H. Yao, and L. Hou, “Image Crowd Counting Using Convolutional Neural Network and Markov Random Field,” J. Adv. Comput. Intell. Intell. Inform., Vol.21, No.4, pp. 632-638, 2017.
  5. [5] Q. Qin and J. Vychodil, “Pedestrian Detection Algorithm Based on Improved Convolutional Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.21, No.5, pp. 834-839, 2017.
  6. [6] Y. Kim, “Convolutional Neural Networks for Sentence Classification,” Proc. of the 2014 Conf. on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746-1751, 2014.
  7. [7] S. Poria, E. Cambria, and A. F. Gelbukh, “Aspect Extraction for Opinion Mining with a Deep Convolutional Neural Network,” Knowledge-Based Systems, Vol.108, pp. 42-49, 2016.
  8. [8] A. Severyn and A. Moschitti, “Twitter Sentiment Analysis with Deep Convolutional Neural Networks,” Proc. of the 38th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 959-962, 2015.
  9. [9] K. Chen, B. Liang, W. Ke, B. Xu, and G. Zeng, “Chinese Micro-Blog sentiment analysis based on multi-channel convolutional neural networks,” J. of Computer Research and Development, Vol.55, No.5, pp. 945-957, 2018 (in Chinese).
  10. [10] V. Mnih, N. Heess, A. Graves, and K. Kavukcuoglu, “Recurrent Models of Visual Attention,” Proc. of the 27th Int. Conf. on Neural Information Processing Systems, Vol.2, pp. 2204-2212, 2014.
  11. [11] D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” Int. Conf. on Learning Representations, 2015.
  12. [12] L. Wang, Z. Cao, G. de Melo, and Z. Liu, “Relation Classification via Multi-Level Attention CNNs,” Proc. of the 54th Annual Meeting of the Association for Computational Linguistics, Vol.1, pp. 1298-1307, 2016.
  13. [13] D. Ma, S. Li, X. Zhang, and H. Wang, “Interactive Attention Networks for Aspect-Level Sentiment Classification,” Proc. of the 26th Int. Joint Conf. on Artificial Intelligence (IJCAI’17), pp. 4068-4074, 2017.
  14. [14] B. Liang, Q. Liu, J. Xu, Q. Zhou, and P. Zhang, “Aspect-Based Sentiment Analysis Based On Multi-Attention CNN,” J. of Computer Research and Development, Vol.54, No.8, pp. 1724-1735, 2017 (in Chinese).
  15. [15] J. Xie, Y. Hou, S. Kang, B. Li, and X. Zhang, “Multi-feature Fusion Based on Semantic Understanding Attention Neural Network for Chinese Text Categorization,” J. of Electronics & Information Technology, Vol.40, No.5, pp. 1258-1265, 2018 (in Chinese).
  16. [16] M. Yang, Q. Qu, X. Chen, C. Guo, Y. Shen, and K. Lei, “Feature-Enhanced Attention Network for Target-Dependent Sentiment Classification,” Neurocomputing, Vol.307, pp. 91-97, 2018.
  17. [17] The Emotional Ontology Library of Dalian University of Technology, [accessed July 10, 2017]
  18. [18] HowNet, [accessed March 16, 2018]

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Last updated on Feb. 17, 2020