Sentiment Analysis of Chinese Evaluate Text Based on Multiple Attention and Feature Fusion Network Model
WANG Yong1，ZHANG Suo-yu2，LV Xin-yi2
1（School of Artificial Intelligence，Chongqing University of Technology，Chongqing 401135，China）2（College of Computer Science and Engineering，Chongqing University of Technology，Chongqing 400054，China）
Abstract：Aiming at the situation that the sentiment analysis of Chinese evaluate text pays less attention to deep emotional semantics，this paper presents a multi-attention and feature fusion network model MTA-CBG.Traditional word vector cannot solve the problem of polysemy，this paper builds a self-attention word vector matrix model to get related features between words.To extract the local features comprehensively by the multi-scale wide convolution structure（MWC）.The two types of features are fused and input into bidirectional gated recurrent unit（BiGRU）to learn serialized features.To acquire wider emotional semantic information while solving the problem of long-distance dependence.Finally，the features are input into the improved Attention-Highway layer to construct sentence-level association，and to extract deep emotional features.Based on comparative experiments of the datasets，the results confirm the means in this paper can exactly enhance the accuracy and [WTB1X]F1[WTB1] score on sentiment analysis of Chinese evaluate text.
王勇,张索宇,吕心怡. 多重注意力特征融合网络对中文评价情感分析[J]. 小型微型计算机系统, 2021, 42(8): 1633-1638.
WANG Yong,ZHANG Suo-yu,LV Xin-yi. Sentiment Analysis of Chinese Evaluate Text Based on Multiple Attention and Feature Fusion Network Model. Journal of Chinese Computer Systems, 2021, 42(8): 1633-1638.
2021, Vol. 42 
