Aspect Level Sentiment Analysis Based on Knowledge Graph and Recurrent Attention Network

被引：0

作者：

Deng L. ^{[1
,2
,3
]}

Wei J. ^{[4
]}

Wu Y. ^{[1
,2
,3
]}

Yu X. ^{[1
,2
,3
]}

Liao X. ^{[1
,2
,3
]}

机构：

[1] College of Mathematics and Computer Science, Fuzhou University, Fuzhou

[2] Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou University, Fuzhou

[3] Digital Fujian Institute of Financial Big Data, Fuzhou

[4] College of Electronics and Information Science, Fujian Jiangxia University, Fuzhou

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Aspect Level Sentiment Analysis; Attention Mechanism; Deep Learning; Knowledge Graph;

D O I：

10.16451/j.cnki.issn1003-6059.202006001

中图分类号：

学科分类号：

摘要：

The existing aspect level sentiment analysis methods cannot solve the problem of polysemous word in different contexts. Therefore, a method for aspect level sentiment analysis based on knowledge graph and recurrent attention network is proposed. The text representation of the bidirectional long short-term memory network is integrated with synonym information in knowledge graph using dynamic attention mechanism to obtain the state vector of knowledge perception. To classify aspect level sentiment, the memory content is constructed by combining the location information and inputting the multi-level gated recurrent unit for calculating the sentiment characteristics of aspect terms. The experimental results show that the proposed method achieves better classification results on three open datasets. © 2020, Science Press. All right reserved.

引用

页码：479 / 487

页数：8

共 21 条

[1]

ELGAMAL M., Sentiment Analysis Methodology of Twitter Data with an Application on HAJJ Season, International Journal of Engineering Research and Science, 2, 1, pp. 82-87, (2016)

[2]

PONTIKI M, GALANIS D, PAYLOPOULOS J, Et al., SemEval-2014 Task 4: Aspect Based Sentiment Analysis, Proc of the 8th International Workshop on Semantic Evaluation, pp. 27-35, (2014)

[3]

LIU B., Sentiment Analysis and Opinion Mining, Synthesis Lectures on Human Language Technologies, 5, 1, pp. 1-167, (2012)

[4]

MOGHADDAM S, ESTER M., Opinion Digger: An Unsupervised Opinion Miner from Unstructured Product Reviews, Proc of the 19th ACM International Conference on Information and Knowledge Management, pp. 1825-1828, (2010)

[5]

KIRITCHENKO S, ZHU X D, CHERRY C, Et al., NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews, Proc of the 8th International Workshop on Semantic Evaluation, pp. 437-442, (2014)

[6]

WAGNER J, ARORA P, CORTES S, Et al., DCU: Aspect-Based Polarity Classification for SemEval Task 4, Proc of the 8th International Workshop on Semantic Evaluation, pp. 223-229, (2014)

[7]

LIU Q, ZHANG H B, ZENG Y F, Et al., Content Attention Model for Aspect Based Sentiment Analysis, Proc of the World Wide Web Conference, pp. 1023-1032, (2018)

[8]

DONG L, WEI F R, TAN C Q, Et al., Adaptive Recursive Neural Network for Target-Dependent Twitter Sentiment Classification, Proc of the 52nd Annual Meeting of the Association for Computatio-nal Linguistics(Short Papers), pp. 49-54, (2014)

[9]

TANG D Y, QIN B, FENG X C, Et al., Effective LSTMs for Target-Dependent Sentiment Classification, Proc of the 26th International Conference on Computational Linguistics(Technical Papers), pp. 3298-3307

[10]

HUANG B X, CARLEY K M., Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification, Proc of the Conference on Empirical Methods in Natural Language Processing, pp. 1091-1096, (2018)

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