[Objective] This paper addresses the challenges facing the traditional static word vector embedding method, aiming to handle polysemy in Chinese texts effectively. It also excavates the contextual emotional features and internal semantic association structure. [Methods] In one channel, we integrated the sentiment elements related to the text into Word2Vec and FastText word vectors through rough data reasoning. We also used CNN to extract the local features of the text. In the other channel, we employed BERT for word embedding supplement and used BiLSTM to obtain the global features of the texts. Finally, we added the attention calculation module for the deep interaction of dual channel features. [Results] The experiment on three Chinese datasets achieved the highest accuracy of 92.43%, representing an improvement of 0.81% over the best value of the benchmark model. [Limitations] The selected datasets are only for modelling coarse-grained sentiment classification. We did not conduct experiments in the fine-grained domain. [Conclusions] The proposed model could effectively improve the performance of Chinese text sentiment classification.
周宁, 钟娜, 靳高雅, 刘斌. 基于混合词嵌入的双通道注意力网络中文文本情感分析*[J]. 数据分析与知识发现, 2023, 7(3): 58-68.
Zhou Ning, Zhong Na, Jin Gaoya, Liu Bin. Chinese Text Sentiment Analysis Based on Dual Channel Attention Network with Hybrid Word Embedding. Data Analysis and Knowledge Discovery, 2023, 7(3): 58-68.
Hearst M A. Direction-Based Text Interpretation as an Information Access Refinement[A]// Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval[M]. 1992: 257-274.
[2]
Xie R B, Yuan X C, Liu Z Y, et al. Lexical Sememe Prediction via Word Embeddings and Matrix Factorization[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. ACM, 2017: 4200-4206.
[3]
Pang B, Lee L, Vaithyanathan S. Thumbs up? Sentiment Classification Using Machine Learning Techniques[C]// Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing. ACM, 2002: 79-86.
( Chen Zhao, Xu Ruifeng, Gui Lin, et al. Combining Convolutional Neural Networks and Word Sentiment Sequence Features for Chinese Text Sentiment Analysis[J]. Journal of Chinese Information Processing, 2015, 29(6): 172-178.)
[5]
Tang D Y, Qin B, Feng X C, et al. Effective LSTMS for Target-Dependent Sentiment Classification[OL]. arXiv Preprint, arXiv: 1512.01100.
[6]
Lu W J, Duan Y, Song Y T. Self-Attention-Based Convolutional Neural Networks for Sentence Classification[C]// Proceedings of the 6th International Conference on Computer and Communications. IEEE, 2021: 2065-2069.
[7]
Mikolov T, Chen K, Corrado G, et al. Efficient Estimation of Word Representations in Vector Space[OL]. arXiv Preprint, arXiv: 1301.3781.
[8]
Pennington J, Socher R, Manning C. GloVe: Global Vectors for Word Representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 1532-1543.
[9]
Bojanowski P, Grave E, Joulin A, et al. Enriching Word Vectors with Subword Information[J]. Transactions of the Association for Computational Linguistics, 2017, 5: 135-146.
doi: 10.1162/tacl_a_00051
[10]
Choi J, Lee S W. Improving FastText with Inverse Document Frequency of Subwords[J]. Pattern Recognition Letters, 2020, 133: 165-172.
doi: 10.1016/j.patrec.2020.03.003
[11]
Kim Y. Convolutional Neural Networks for Sentence Classification[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 1746-1751.
[12]
Dong M, Li Y F, Tang X, et al. Variable Convolution and Pooling Convolutional Neural Network for Text Sentiment Classification[J]. IEEE Access, 2020, 8: 16174-16186.
doi: 10.1109/Access.6287639
[13]
Li L Q, Yang L, Zeng Y Y. Improving Sentiment Classification of Restaurant Reviews with Attention-Based Bi-GRU Neural Network[J]. Symmetry, 2021, 13(8): 1517.
doi: 10.3390/sym13081517
( Cheng Yan, Sun Huan, Chen Haomai, et al. Text Sentiment Analysis Capsule Model Combining Convolutional Neural Network and Bidirectional GRU[J]. Journal of Chinese Information Processing, 2021, 35(5): 118-129.)
( Han Pu, Zhang Wei, Zhang Zhanpeng, et al. Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel[J]. Data Analysis and Knowledge Discovery, 2021, 5(11): 68-79.)
[16]
Cheng Y, Yao L B, Xiang G X, et al. Text Sentiment Orientation Analysis Based on Multi-Channel CNN and Bidirectional GRU with Attention Mechanism[J]. IEEE Access, 2020, 8: 134964-134975.
doi: 10.1109/Access.6287639
[17]
Peters M, Neumann M, Iyyer M, et al. Deep Contextualized Word Representations[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. 2018: 2227-2237.
[18]
Devlin J, Chang M W, Lee K, et al. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding[OL]. arXiv Preprint, arXiv: 1810.04805.
[19]
Man R, Lin K. Sentiment Analysis Algorithm Based on BERT and Convolutional Neural Network[C]// Proceedings of the 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers. IEEE, 2021: 769-772.
[20]
Mihalcea R, Tarau P. TextRank: Bringing Order into Text[C]// Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. 2004: 404-411.
[21]
Yan S, Yan L, Wu J Z. Rough Data-Deduction Based on the Upper Approximation[J]. Information Sciences, 2016, 373: 308-320.
doi: 10.1016/j.ins.2016.09.011
[22]
闫硕. 基于上近似的粗糙数据推理研究及应用[D]. 北京: 北京交通大学, 2017.
[22]
Yan Shuo. Rough Data-Deduction Based on the Upper Approximation and Its Applications[D]. Beijing: Beijing Jiaotong University, 2017.)
( Zhou Ning, Shi Wenqian, Zhu Zhaozhao. TextRank Keyword Extraction Algorithm Based on Rough Data-Deduction[J]. Journal of Chinese Information Processing, 2020, 34(9): 44-52.)
[24]
Turney P D, Littman M L. Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus[OL]. arXiv Preprint, arXiv: 0212012.
( Zhu Xinhua, Ma Runcong, Sun Liu, et al. Word Semantic Similarity Computation Based on HowNet and CiLin[J]. Journal of Chinese Information Processing, 2016, 30(4): 29-36.)
( Xie Siya, Shi Yiping, Hu Jialing, et al. Research on Emotional Classification of Weibo Users Based on BiLSTM-ATT[J]. Transducer and Microsystem Technologies, 2021, 40(2): 26-29.)
[27]
Tan S B, Zhang J. An Empirical Study of Sentiment Analysis for Chinese Documents[J]. Expert Systems with Applications, 2008, 34(4): 2622-2629.
doi: 10.1016/j.eswa.2007.05.028
( Chen Bo, Xie Jun, Miao Duoqian, et al. Chinese Text Sentiment Feature Analysis Based on Rough Set and Multi Channel Word Vector[J]. Journal of Chinese Information Processing, 2020, 34(8): 94-104.)
[29]
Feng Y, Cheng Y. Short Text Sentiment Analysis Based on Multi-Channel CNN with Multi-Head Attention Mechanism[J]. IEEE Access, 2021, 9: 19854-19863.
doi: 10.1109/Access.6287639
( Yang Lu, He Mingxiang. Chinese Text Sentiment Analysis Model Based on Gated Mechanism and Convolutional Neural Network[J]. Journal of Computer Applications, 2021, 41(10): 2842-2848.)
doi: 10.11772/j.issn.1001-9081.2020122043
