[1]马晓慧,马尚才,闫俊伢,等.基于距离感知的目标情感分类模型[J].南京师大学报(自然科学版),2021,44(04):111-116.[doi:10.3969/j.issn.1001-4616.2021.04.014]
　Ma Xiaohui,Ma Shangcai,Yan Junya,et al.Distance-Based Model for Target-Level Sentiment Analysis[J].Journal of Nanjing Normal University(Natural Science Edition),2021,44(04):111-116.[doi:10.3969/j.issn.1001-4616.2021.04.014]
点击复制

基于距离感知的目标情感分类模型()
分享到：

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第44卷
期数:
2021年04期
页码:
111-116
栏目:
·计算机科学与技术·
出版日期:
2021-12-15

文章信息/Info

Title:
Distance-Based Model for Target-Level Sentiment Analysis
文章编号:
1001-4616(2021)04-0111-06
作者:
马晓慧1; 马尚才2; 闫俊伢1; 陈 波3
(1.山西大学商务学院,山西 太原 030031)(2.山西财经大学信息管理学院,山西 太原 030006)(3.山东理工大学计算机科学与技术学院,山东 淄博 255020)
Author(s):
Ma Xiaohui1; Ma Shangcai2; Yan Junya1; Chen Bo3
(1.Business College,Shanxi University,Taiyuan 030031,China)(2.Faculty of Information Management,Shanxi University of Finance and Economics,Taiyuan 030006,China)(3.School of Computer Science and Technology,Shandong University of Technology,Zibo 255020,China)
关键词:
词嵌入; 距离信息; 卷积神经网络; 目标情感分类
Keywords:
word embedding; distance information; convolutional neural network; target-level sentiment analysis
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2021.04.014
文献标志码:
A
摘要:
目标情感分类是是一种细粒度的情感分类任务,针对人工生成特征模型成本高且不能捕捉上下文语义、传统循环神经网络模型训练时间长等问题,设计了一种基于距离感知的目标情感分类模型. 通过距离感知窗口对目标词与邻近词之间的距离信息进行建模,结合词嵌入技术,分别对输入文本和距离信息建立向量矩阵,使用卷积神经网络提取特征,将文本语义特征和距离特征结合,输入到分类层进行目标情感分类. 最后在Sem2014笔记本电脑和餐厅两个数据集上进行实验,取得了比基于循环神经网络生成特征的模型和利用外部语法分析器生成特征的模型更好的分类效果,且具有更短的模型训练时间. 研究结果对目标情感分类领域的应用具有参考价值.
Abstract:
Target-level sentiment analysis is a fine-grained classification task. Aiming at the problems such as high cost of manually generated feature model,inability to capture context semantics,and long training time of recurrent neural network,a target-level sentiment analysis model based on distance is designed. The distance information between the target word and its neighbors is modeled through the distance perception window. Combined with the word embedding technology,the matrix-vector is established for the input text and distance information,respectively. The features are extracted using the convolutional neural network,and the semantic features of the text are combined with the distance features. Finally,experimental results achieved on a Sem 2014 dataset(Laptop and Restaurant)show that our approach achieves a significant improvement in the accuracy over the comparison models and has a shorter model training time. The research results have reference value for the applied research of target sentiment classification.

参考文献/References:

[1] WANG S,MAZUMDER S,LIU B,et al. Target-sensitive memory networks for aspect sentiment classification[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Melbourne:ACL,2018:957-967.
[2]PONTIKI M,GALANIS D,PAPAGEORGIOU H,et al. Sem-2016 task 5:Aspect based sentiment analysis[C]//Proceedings of the 10th International Workshop on Semantic uation. San Diego:ACL,2016:19-30.
[3]WANG B,LIAKATA M,ZUBIAGA A,et al. TDParse:Multi-target-specific sentiment recognition on Twitter[C]//Conference of the European Chapter of the Association for Computational Linguistics. Valencia:ACL,2017:483-493.
[4]VO D T,ZHANG Y. Target-dependent twitter sentiment classification with rich automatic features[C]//Proceedings of the 24th International Conference on Artificial Intelligence. Buenos Aires:AAAI Press,2015:1347-1353.
[5]易顺明,周洪斌,周国栋. Twitter推文与情感词典SentiWordNet匹配算法研究[J]. 南京师范大学学报(工程技术版),2016,16(3):41-47,53.
[6]陈文实,刘心惠,鲁明羽. 基于编码解码器与深度主题特征抽取的多标签文本分类[J]. 南京师大学报(自然科学版),2019,42(4):61-68.
[7]王俊淑,张国明,胡斌. 基于深度学习的推荐算法研究综述[J]. 南京师范大学学报(工程技术版),2018,18(4):33-43.
[8]TOH Z Q,SU J. NLANGP:Supervised machine learning system for aspect category classification and opinion target extraction[C]//Proceedings of the 9th International Workshop on Semantic uation. Denver:ACL,2015:496-501.
[9]MANEK A S,SHENOY P D,MOHAN M C,et al. Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier[J]. World wide web,2017,20(2):135-154.
[10]PARKHE V,BISWAS B. Sentiment analysis of movie reviews:finding most important movie aspects using driving factors[J]. Soft computing,2016,20(9):3373-3379.
[11]LIU J M,ZHANG Y. Attention modeling for targeted sentiment[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics:Volume 2,Short Papers. Valencia:ACL,2017:572-577.
[12]曹卫东,李嘉琪,王怀超. 采用注意力门控卷积网络模型的目标情感分析[J]. 西安电子科技大学学报,2019,46(6):30-36.
[13]梁斌,刘全,徐进,等. 基于多注意力卷积神经网络的特定目标情感分析[J]. 计算机研究与发展,2017,54(8):1724-1735.
[14]武婷,曹春萍. 融合位置权重的基于注意力交叉注意力的长短期记忆方面情感分析模型[J]. 计算机应用,2019,39(8):2198-2203.
[15]TANG D Y,QIN B,FENG X C,et al. Effective LSTMs for target-dependent sentiment classification[C]//Proceedings of COLING 2016,the 26th International Conference on Computational Linguistics. Osaka:The COLING 2016 Organizing Committee,2016:3298-3307.
[16]李丽双,周安桥,刘阳,等. 基于动态注意力GRU的特定目标情感分类[J]. 中国科学:信息科学,2019,49(8):1019-1030.
[17]JEBBARA S,CIMIANO P. Aspect-based sentiment analysis using a two-step neural network architecture[C]//Semantic Web uation Challenge. Heraklion:Springer,2016:153-167.
[18]WANG Y Q,HUANG M L,ZHU X Y,et al. Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austin:ACL,2016:606-615.
[19]张新生,高腾. 多头注意力记忆网络的对象级情感分类[J]. 模式识别与人工智能,2019,32(11):997-1005.
[20]陈思远,彭超,蔡林森,等. 一种用于特定目标情感分析的深度网络模型[J]. 计算机工程,2019,45(3):286-292.
[21]KALCHBRENNERK N,GREFENSTETTE E,BLUNSOM P. A convolutional neural network for modelling sentences[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Baltimore:ACL,2014:655-665.
[22]KIRITCHENKO S,ZHU X D,CHERRY C,et al. NRC-Canada-2014:Detecting aspects and sentiment in customer reviews[C]//Proceedings of the 8th International Workshop on Semantic uation(Sem 2014). Dublin:ACL,2014:437-442.

相似文献/References:

[1]尹帮治,徐 健,唐超尘.基于图神经网络与表示学习的文本情感分析[J].南京师大学报(自然科学版),2024,(03):97.[doi:10.3969/j.issn.1001-4616.2024.03.012]
　Yin Bangzhi,Xu Jian,Tang Chaochen.Text Sentiment Analysis Based on Graph Neural Networks and Representation Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2024,(04):97.[doi:10.3969/j.issn.1001-4616.2024.03.012]

备注/Memo

备注/Memo:
收稿日期:2021-03-31.
基金项目:教育部产学合作协同育人项目(201902167003、 201902084012)、山西省软科学研究计划项目(2019041057-1)、山西省高等学校教学改革创新项目(J2020440).
通讯作者:马晓慧,副教授,研究方向:计算机应用技术、情感分析与观点挖掘. E-mail:mxh1112@163.com
更新日期/Last Update: 2021-12-15