Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network

doi:10.3390/electronics11121906

CQUET-IR

	Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network
	Mao, Yanying 1,2; Zhang, Yu 3,4; Jiao, Liudan 3; Zhang, Heshan 3
	2022-06
发表期刊	ELECTRONICS
EISSN	2079-9292
卷号	11 期号:12
摘要	Due to outstanding feature extraction ability, neural networks have recently achieved great success in sentiment analysis. However, one of the remaining challenges of sentiment analysis is to model long texts to consider the intrinsic relations between two sentences in the semantic meaning of a document. Moreover, most existing methods are not powerful enough to differentiate the importance of different document features. To address these problems, this paper proposes a new neural network model: AttBiLSTM-2DCNN, which entails two perspectives. First, a two-layer, bidirectional long short-term memory (BiLSTM) network is utilized to obtain the sentiment semantics of a document. The first BiLSTM layer learns the sentiment semantic representation from both directions of a sentence, and the second BiLSTM layer is used to encode the intrinsic relations of sentences into the document matrix representation with a feature dimension and a time-step dimension. Second, a two-dimensional convolutional neural network (2DCNN) is employed to obtain more sentiment dependencies between two sentences. Third, we utilize a two-layer attention mechanism to distinguish the importance of words and sentences in the document. Last, to validate the model, we perform an experiment on two public review datasets that are derived from Yelp2015 and IMDB. Accuracy, F1-Measure, and MSE are used as evaluation metrics. The experimental results show that our model can not only capture sentimental relations but also outperform certain state-of-the-art models.
关键词	sentiment analysis bidirectional LSTM 2DCNN attention mechanism
DOI	10.3390/electronics11121906
收录类别	SCIE
语种	英语
WOS研究方向	Computer Science ; Engineering ; Physics
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Physics, Applied
WOS记录号	WOS:000816344300001
出版者	MDPI
原始文献类型	Article ; 期刊论文
出版地	BASEL
引用统计	被引频次：20[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/13936
专题	重庆电子科技职业大学
作者单位	1.Chongqing Coll Elect Engn, Dept Commun Engn, Chongqing 401331, Peoples R China; 2.Chongqing Univ Posts & Telecommun, Coll Comp Sci & Technol, Chongqing, Peoples R China; 3.Chongqing Jiaotong Univ, Sch Econ & Management, Chongqing 400074, Peoples R China; 4.TY Lin Int Engn Consulting China Co Ltd, Chongqing 401121, Peoples R China
推荐引用方式 GB/T 7714	Mao, Yanying,Zhang, Yu,Jiao, Liudan,et al. Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network[J]. ELECTRONICS,2022,11(12).
APA	Mao, Yanying,Zhang, Yu,Jiao, Liudan,&Zhang, Heshan.(2022).Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network.ELECTRONICS,11(12).
MLA	Mao, Yanying,et al."Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network".ELECTRONICS 11.12(2022).