[1]胡任远,刘建华,王璇,等.引入双循环机制深度学习模型的文本情感分析[J].福建工程学院学报,2022,20(04):383-390.[doi:10.3969/j.issn.1672-4348.2022.04.013]
　HU Renyuan,LIU Jianhua,WANG Xuan,et al.Text sentiment analysis that introduces a double recurrent mechanism deep learning model[J].Journal of FuJian University of Technology,2022,20(04):383-390.[doi:10.3969/j.issn.1672-4348.2022.04.013]
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引入双循环机制深度学习模型的文本情感分析()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第20卷
期数:
2022年04期
页码:
383-390
栏目:
出版日期:
2022-08-25

文章信息/Info

Title:
Text sentiment analysis that introduces a double recurrent mechanism deep learning model
作者:
胡任远; 刘建华; 王璇; 罗逸轩; 林鸿辉
福建工程学院计算机科学与数学学院
Author(s):
HU Renyuan;  LIU Jianhua;  WANG Xuan;  LUO Yixuan;  LIN Honghui
School of Information Science and Engineering, Fujian University of Technology
关键词:
双向长短期记忆神经网络; 双循环卷积神经网络; 注意力机制; 文本情感分析
Keywords:
bidirectional long and short-term memory neural network;  double recurrent convolution neural network;  attention mechanism;  text sentiment analysis
分类号:
TP391.1
DOI:
10.3969/j.issn.1672-4348.2022.04.013
文献标志码:
A
摘要:
深度神经网络模型通常使用注意力机制或融合卷积神经网络来提取特征，但由于注意力机制抓取的特征过于单一，存在提取特征不完善的问题。 将循环机制引入卷积神经网络中，构建了具有双循环结构的网络模型(DRCNN)，从而改善模型的特征提取能力，将其与双向长短期记忆网络结合，提出一种带有注意力机制、特征提取能力更强的混合模型(BiLSTM-DRCNN)并应用于情感分类任务中。通过情感分类的实验分析表明，BiLSTM-DRCNN 神经网络模型具有比较好的性能，与融合卷积神经网络(CNN)和双循环长短期记忆神经网络(BiLSTM)模型相比，综合评价指标提高2%以上；与BiLSTM-CNN、Fusion Model 模型相比，综合评价指标提高了近1%，且收敛速度更快。
Abstract:
Deep neural network models usually use attention mechanism or fusion convolutional neural network for feature extraction. However, due to the fact that the features grasped by the attention mechanism are too simplified, there is a problem that the extraction features are not perfect. Therefore, the recurrent mechanism was introduced into the convolutional neural network, and a double recurrent convolutional neural network model was constructed to improve the feature extraction ability of the model. Then the model was combined with the bidirectional long and short-term memory network, so as to put forward a hybrid model with the attention mechanism and stronger feature extraction capability (BiLSTM-CNN), which was applied to sentiment classification tasks. Experimental analysis of sentiment classification shows that the BiLSTM-DRCNN neural network model has good performance, and the comprehensive evaluation index is increased by more than 2% compared with the fusion convolutional neural network (CNN) and the double-loop long short-term memory neural network (BiLSTM) model. Compared with the BiLSTM-CNN and Fusion Model models, the comprehensive evaluation index is improved by nearly 1%, and the convergence is faster.

参考文献/References:

[1] BOIY E, MOENS M F. A machine learning approach to sentiment analysis in multilingual Web texts[J]. Information Retrieval, 2009, 12（5）: 526-558.[2] ZHANG L, WANG S, LIU B. Deep learning for sentiment analysis: a survey[J]. WIREs Data Mining and Knowledge Discovery, 2018, 8（4）: e1253.[3] KIM S M, HOVY E. Identifying and analyzing judgment opinions[C]∥Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics. Morristown, NJ, USA: Association for Computational Linguistics, 2006: 200-207.[4] 〖JP3〗KIM Y. Convolutional neural networks for sentence classification[C]∥Proceedings of the2014 Conference on Empirical Methods in Natural Language Processing（EMNLP）. Stroudsburg, PA, USA: Association for Computational Linguistics, 2014: 1408.5882.[5] 梁军, 柴玉梅, 原慧斌, 等. 基于极性转移和LSTM递归网络的情感分析[J]. 中文信息学报, 2015, 29（5）: 152-159.[6] WANG J, YU L C, LAI K R, et al. Dimensional sentiment analysis using a regional CNN-LSTM model[C]∥Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics （Volume 2: Short Papers）. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016: 225-230.[7] 李洋, 董红斌. 基于CNN和BiLSTM网络特征融合的文本情感分析[J]. 计算机应用, 2018, 38（11）: 3075-3080.[8] LAI S W, XU L H, LIU K, et al. Recurrent convolutional neural networks for text classification [J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2015, 29（1）: 2267-2273.[9] 刘全, 梁斌, 徐进, 等. 一种用于基于方面情感分析的深度分层网络模型[J]. 计算机学报, 2018, 41（12）: 2637-2652.[10] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9（8）: 1735-1780.[11] HINTON G E, SRIVASTAVE N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors[J]. Computer Science, 2012, 3(4): 212-223.[12] WANG Y Q, HUANG M L, ZHU X Y, et al. Attention-based LSTM for aspect-level sentiment classification[C]∥Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016: 606-615. [13] 〖JP4〗ZHAO Z W，WU Y Z. Attention-based convolutional neural networks for sentence classification[C]∥Interspeech 2016. ISCA：ISCA，2016：705-709.[14] ZHOU P, SHI W, TIAN J, et al. Attention-based bidirectional long short-term memory networks for relation classification[C]∥Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics （Volume 2: Short Papers）. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016: 207-212.[15] YIN W P, SCHTZE H, XIANG B, et al. ABCNN: attention-based convolutional neural network for modeling sentence pairs[EB/OL]. 2015: arXiv: 1512.05193[cs.CL]. https: ∥arxiv.org/abs/1512.05193.[16] BIN Y, YANG Y, SHEN F M, et al. Describing video with attention-based bidirectional LSTM[J]. IEEE Transactions on Cybernetics, 2019, 49（7）: 2631-2641.[17] 赵宏, 王乐, 王伟杰. 基于BiLSTM-CNN串行混合模型的文本情感分析[J]. 计算机应用, 2020, 40（1）: 16-22.

更新日期/Last Update: 2022-08-25