为有效利用句子中的句法以及语义特征,本文提出基于句法结构与语义分析的方面级情感分析模型(Aspect-level sentiment analysis method based on syntactic structure and semantic analysis, LCF-Sync)。具体来说,LCF-Sync根据句子中...为有效利用句子中的句法以及语义特征,本文提出基于句法结构与语义分析的方面级情感分析模型(Aspect-level sentiment analysis method based on syntactic structure and semantic analysis, LCF-Sync)。具体来说,LCF-Sync根据句子中单词与方面之间的距离获取单词与方面的词法特征,并构建单词节点之间的句法依赖树获取句法特征;LCF-Sync利用多头自注意力机制获取句子中单词之间的语义特征,同时融合局部特征与全局特征进行情感分析预测。本文在三个基准数据集上进行大量实验,实验结果表明本文提出的模型优于基线方法。展开更多
Sentiment analysis,commonly called opinion mining or emotion artificial intelligence(AI),employs biometrics,computational linguistics,nat-ural language processing,and text analysis to systematically identify,extract,m...Sentiment analysis,commonly called opinion mining or emotion artificial intelligence(AI),employs biometrics,computational linguistics,nat-ural language processing,and text analysis to systematically identify,extract,measure,and investigate affective states and subjective data.Sentiment analy-sis algorithms include emotion lexicon,traditional machine learning,and deep learning.In the text sentiment analysis algorithm based on a neural network,multi-layer Bi-directional long short-term memory(LSTM)is widely used,but the parameter amount of this model is too huge.Hence,this paper proposes a Bi-directional LSTM with a trapezoidal structure model.The design of the trapezoidal structure is derived from classic neural networks,such as LeNet-5 and AlexNet.These classic models have trapezoidal-like structures,and these structures have achieved success in the field of deep learning.There are two benefits to using the Bi-directional LSTM with a trapezoidal structure.One is that compared with the single-layer configuration,using the of the multi-layer structure can better extract the high-dimensional features of the text.Another is that using the trapezoidal structure can reduce the model’s parameters.This paper introduces the Bi-directional LSTM with a trapezoidal structure model in detail and uses Stanford sentiment treebank 2(STS-2)for experiments.It can be seen from the experimental results that the trapezoidal structure model and the normal structure model have similar performances.However,the trapezoidal structure model parameters are 35.75%less than the normal structure model.展开更多
文摘为有效利用句子中的句法以及语义特征,本文提出基于句法结构与语义分析的方面级情感分析模型(Aspect-level sentiment analysis method based on syntactic structure and semantic analysis, LCF-Sync)。具体来说,LCF-Sync根据句子中单词与方面之间的距离获取单词与方面的词法特征,并构建单词节点之间的句法依赖树获取句法特征;LCF-Sync利用多头自注意力机制获取句子中单词之间的语义特征,同时融合局部特征与全局特征进行情感分析预测。本文在三个基准数据集上进行大量实验,实验结果表明本文提出的模型优于基线方法。
基金supported by Yunnan Provincial Education Department Science Foundation of China under Grant construction of the seventh batch of key engineering research centers in colleges and universities(Grant Project:Yunnan College and University Edge Computing Network Engineering Research Center).
文摘Sentiment analysis,commonly called opinion mining or emotion artificial intelligence(AI),employs biometrics,computational linguistics,nat-ural language processing,and text analysis to systematically identify,extract,measure,and investigate affective states and subjective data.Sentiment analy-sis algorithms include emotion lexicon,traditional machine learning,and deep learning.In the text sentiment analysis algorithm based on a neural network,multi-layer Bi-directional long short-term memory(LSTM)is widely used,but the parameter amount of this model is too huge.Hence,this paper proposes a Bi-directional LSTM with a trapezoidal structure model.The design of the trapezoidal structure is derived from classic neural networks,such as LeNet-5 and AlexNet.These classic models have trapezoidal-like structures,and these structures have achieved success in the field of deep learning.There are two benefits to using the Bi-directional LSTM with a trapezoidal structure.One is that compared with the single-layer configuration,using the of the multi-layer structure can better extract the high-dimensional features of the text.Another is that using the trapezoidal structure can reduce the model’s parameters.This paper introduces the Bi-directional LSTM with a trapezoidal structure model in detail and uses Stanford sentiment treebank 2(STS-2)for experiments.It can be seen from the experimental results that the trapezoidal structure model and the normal structure model have similar performances.However,the trapezoidal structure model parameters are 35.75%less than the normal structure model.
