Target Embedding and Position Attention with LSTM for Aspect Based Sentiment Analysis

Borun Xu, Xiaoxiao Wang, Bo Yang, Zhongfeng Kang
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引用次数: 13

Abstract

Aspect Based Sentiment Analysis (ABSA) provides fine-grained sentiment information compared with traditional sentiment analysis. There are two approaches to the task, aspect term sentiment analysis (ATSA) and aspect category sentiment analysis (ACSA). In this paper, we propose a model, namely Recurrent Neural Network with Target Embedding (RTE) using target enhance technic to improve the accuracy of both the two kinds of approaches. Specifically, RTE involves two stacked LSTMs for target term extraction and sentiment analysis, and a target enhance unit for spreading target or aspect information. Experiments are conducted on several public datasets and the results illustrate that the proposed RTE outperforms several state-of-the-art models compared.
面向面向方面情感分析的LSTM目标嵌入和位置关注
与传统的情感分析相比,基于方面的情感分析(ABSA)提供了细粒度的情感信息。有两种方法:方面项情感分析(ATSA)和方面类别情感分析(ACSA)。在本文中,我们提出了一个模型,即递归神经网络与目标嵌入(RTE),利用目标增强技术来提高这两种方法的准确性。具体来说,RTE包括两个堆叠的lstm,用于目标词提取和情感分析,以及一个目标增强单元,用于传播目标或方面信息。在几个公共数据集上进行了实验,结果表明,所提出的RTE优于几种最先进的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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