基于分层注意力机制的细粒度情感分析
Fine-Grained Sentiment Analysis Based on Hierarchical Attention Networks
DOI: 10.12677/CSA.2019.911240, PDF,  被引量   
作者: 邵兴林*, 牛少彰：北京邮电大学智能通信软件与多媒体北京重点实验室，北京
关键词: 注意力机制；LSTM；细粒度情感分析；Attention Mechanism； LSTM； Fine-Grained Sentiment Analysis
摘要: 细粒度情感分析从多个角度对文本情感极性进行分析，越来越成为情感分析领域的热点问题。不同于以往算法使用属性信息作为嵌入向量和单层注意力机制的LSTM网络，本文提出了一种基于分层注意力机制的多层网络，一方面可以对单词和句子分别给予不同的注意力权重，帮助模型增加对重要部分的注意力，另一方面使用实体信息作为嵌入向量，比属性信息更能表示目标短语的含义。实验结果表明，该模型在SemEval 2014数据集上取得了出色的效果，优于现有的算法。
Abstract: Fine-grained sentiment analysis analyzes the emotional polarity of text from multiple angles, and it has become a hot issue in the field of sentiment analysis. Different from previous LSTM networks in which the algorithm uses attribute information as the embedded vector and single-layer attention mechanism, this paper proposes a multi-layer network based on hierarchical attention mechanism, which can give different attention weights to words and sentences. To help the model increase the attention to important parts, on the other hand use the entity information as the embedded vector, which is more representative of the meaning of the target phrase than the attribute information. The experimental results show that the model has achieved excellent results on the SemEval 2014 dataset and is superior to the existing algorithms.
文章引用：邵兴林, 牛少彰. 基于分层注意力机制的细粒度情感分析[J]. 计算机科学与应用, 2019, 9(11): 2143-2153. https://doi.org/10.12677/CSA.2019.911240

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