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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (12): 37-47    DOI: 10.11925/infotech.2096-3467.2021.0554
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Aspect-Level Sentiment Analysis Based on BAGCNN
Yu Bengong1,2(),Zhang Shuwen1
1School of Management, Hefei University of Technology, Hefei 230009, China
2Key Laboratory of Process Optimization & Intelligent Decision-making (Hefei University of Technology),Ministry of Education, Hefei 230009, China
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[Objective] This paper proposes a BERT-based Attention Gated Convolution Neural Network model (BAGCNN), aiming to improve the traditional aspect-level sentiment analysis algorithm. [Methods] First, the pre-trained BERT model generated feature representation for the texts and aspect words. Then, we introduced the Multi Head Self-attention Mechanism to solve the problem of long-distance dependence of aspect words. Finally, we selectively extracted the multi-level context features paralleling the aspect words with the Gated Convolution Neural Network. [Results] Compared to the benchmark model, the accuracy of our new model was improved by 4.24, 4.01 and 3.89 percentage points on restaurant, laptop and twitter datasets. The size of the downstream parallel structure of the model was also reduced by 1.27 MB. [Limitations] The proposed model did not work well with data sets having significantly different text length. [Conclusions] The new BAGCNN model could effectively remove the context information irrelevant to the aspect words.

Key wordsAspect-Level Sentiment Analysis      BERT      Multi Head Self-attention Mechanism      Gated Convolution Neural Network     
Received: 13 June 2021      Published: 20 January 2022
ZTFLH:  TP391  
Fund:National Natural Science Foundation of China(72071061);National Natural Science Foundation of China(71671057)
Corresponding Authors: Yu Bengong,ORCID:0000-0003-4170-2335     E-mail:

Cite this article:

Yu Bengong, Zhang Shuwen. Aspect-Level Sentiment Analysis Based on BAGCNN. Data Analysis and Knowledge Discovery, 2021, 5(12): 37-47.

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Aspect-Level Sentiment Analysis Model Based on BAGCNN
数据集 类型 positive neural negative
Restaurant train 2 164 637 807
test 728 196 196
Laptop train 994 464 879
test 341 169 128
Twitter train 1 561 3 127 1 560
test 173 346 173
Dataset Statistics
dropout rate 0.1
batch size 32
epoch 6
optimizer Adam
learning rate 2e-5
Hyperparameter Setting
Model Restaurant Laptop Twitter
Accuracy F1 Accuracy F1 Accuracy F1
GCAE 77.32 63.78 66.04 57.35 72.16 69.87
AGCN 78.84 68.78 72.10 67.52 70.28 68.56
Mul-AT-CNN 79.46* - 75.39* - 71.25* -
AOA 79.46 68.80 73.51 68.18 71.96 69.91
IAN 79.46 69.75 72.47 67.18 69.85 68.27
MAN 80.71* 70.95* 74.13* 71.93* 72.12* 70.13*
ASGCN 81.28 71.74 74.81 70.74 72.40 70.68
BERT-SPC 83.02 74.55 78.06 73.61 73.12 71.83
BERT-AEN 81.98 72.16 77.18 73.83 72.98 71.74
TD-BERT 84.11 76.96 77.43 73.54 75.43 74.38
Our(BAGCNN) 84.95 77.90 78.14 74.14 76.01 74.61
Model Comparison Results(%)
Influence of Convolution Kernel Size on Model
The Influence of Different Word Embedding Coding Methods on the Model
The Influence of Different Feature Extraction Methods on Model
Models Params× 10 6 Memory(MB)
ATAE-LSTM 2.53 13.76
AOA 2.89 15.15
IAN 2.17 12.40
GloVe-LSTM-ATT 2.07 16.90
AEN-GloVe 1.16 11.04
ASGCN 0.45 5.84
AGCN 1.36 13.42
GCAE 0.82 11.38
Our(GloVe-AGCNN) 1.02 11.13
Model Sizes
The Effect of Gated Convolution
文本 方法 结果
1. Even when the chef is not in the house, the food and service are right on target. Aspect chef(O) food(P) service(P)
2. Food was average and creme brulee was awful - the sugar was charred, not caramelized and smelled of kerosene. Aspect Food(O) brulee(N) sugar(N)
3. The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it 's on the menu or not. Aspect food(P) kitchen(P) menu(O)
Prediction Results of the Models
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