@Article{cmc.2020.09913,
AUTHOR = {Ao Feng, Zhengjie Gao, Xinyu Song, Ke Ke, Tianhao Xu, Xuelei Zhang},
TITLE = {Modeling Multi-Targets Sentiment Classification via Graph Convolutional Networks and Auxiliary Relation},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {909--923},
URL = {http://www.techscience.com/cmc/v64n2/39336},
ISSN = {1546-2226},
ABSTRACT = {Existing solutions do not work well when multi-targets coexist in a sentence. 
The reason is that the existing solution is usually to separate multiple targets and process 
them separately. If the original sentence has N target, the original sentence will be 
repeated for N times, and only one target will be processed each time. To some extent, 
this approach degenerates the fine-grained sentiment classification task into the sentencelevel sentiment classification task, and the research method of processing the target 
separately ignores the internal relation and interaction between the targets. Based on the 
above considerations, we proposes to use Graph Convolutional Network (GCN) to model 
and process multi-targets appearing in sentences at the same time based on the positional 
relationship, and then to construct a graph of the sentiment relationship between targets
based on the difference of the sentiment polarity between target words. In addition to the 
standard target-dependent sentiment classification task, an auxiliary node relation 
classification task is constructed. Experiments demonstrate that our model achieves new 
comparable performance on the benchmark datasets: SemEval-2014 Task 4, i.e., reviews 
for restaurants and laptops. Furthermore, the method of dividing the target words into 
isolated individuals has disadvantages, and the multi-task learning model is beneficial to 
enhance the feature extraction ability and expression ability of the model.},
DOI = {10.32604/cmc.2020.09913}
}