方面级情感分析旨在明确文本中关于特定方面的情感极性.针对句中方面词由复杂词组组成造成方面情感极性判断错误的问题,论文提出了一种融合注意力机制的胶囊网络方面级情感分析模型.模型首先通过双向长短时记忆网络(Bi-directional Long Short-Term Memory,BiLSTM)提取序列语义信息,使用N-gram模型对序列语义信息中的目标方面进行编码,然后利用交互注意力机制学习方面词和上下文之间的注意力,将最终生成的文本表示接入融合方面特征表示的胶囊网络进行分类,得到文本方面级的情感分类结果.模型利用胶囊网络有效提取部分与整体关系特征的能力,融合N-gram模型提取到的方面特征变换矩阵,改进了传统动态路由方法,增强了模型对方面情感极性的判断能力.该模型在SemEval-2014餐馆数据集和笔记本数据集上与多个模型进行了对比分析,实验结果显示该模型在两个数据集上的精确度达到了78.4%和72.4%,F1分数分别为0.687和0.668,证明融合交互注意力机制的胶囊网络模型在方面级情感分析任务方面具有较强的分类效果.
Joint Attention Mechanism and Capsule Network for Aspect-level Sentiment Analysis
Aspect level sentiment analysis aims to clarify the emotional polarity of specific aspects in the text.Aiming at the problem that aspect words in sentences are composed of complex phrases,which leads to the wrong judgment of aspect emotion po-larity,this paper proposes a model based on attention mechanism and capsule network for aspect level sentient classification(AS-ATTCaps).The model first extracts the sequence semantic information through bi-directional long short term memory(BiL-STM),then encodes the target aspect in the sequence semantic information using N-gram model,and then uses the interactive at-tention mechanism to learn the attention between aspect words and context.The final generated text representation is connected to the capsule network of fusion aspect feature representation for classification,and the emotion classification results of text aspect lev-el are obtained.In this model,the capsule network is used to effectively extract the relationship between part and the whole,and the aspect feature transformation matrix extracted by N-gram model is integrated to improve the traditional dynamic routing method and enhance the judgment ability of the model on aspect emotional polarity.The experimental results show that the accuracy of the model on the two datasets reaches 78.4%and 72.4%,and the F1 scores are 0.687 and 0.668 respectively,which proves that the capsule network model with interactive attention mechanism has a strong classification effect in aspect level emotion analysis task.
aspect-based sentiment analysisnatural language processingcapsule networkattentional mechanism
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