|
|
论文:2019,Vol:37,Issue(3):558-564 |
|
|
引用本文： |
|
|
周陈超, 陈群, 李战怀, 赵波, 胥勇军, 秦阳. 基于注意力和双向LSTM的评价对象类别判定[J]. 西北工业大学学报 |
|
|
ZHOU Chenchao, CHEN Qun, LI Zhanhuai, ZHAO Bo, XU Yongjun, QIN Yang. Aspect Category Detection Based on Attention Mechanism and Bi-Directional LSTM[J]. Northwestern polytechnical university |
|
|
|
|
|
|
|
| 基于注意力和双向LSTM的评价对象类别判定 |
|
| 周陈超1,2, 陈群1,2, 李战怀1,2, 赵波3, 胥勇军3, 秦阳3 |
|
1. 西北工业大学 计算机学院, 陕西 西安 710072;
2. 西北工业大学 大数据存储与管理工业和信息化部重点实验室, 陕西 西安 710072;
3. 中国人民解放军95806部队, 北京 100076 |
| 摘要: |
| 在线评论在用户的购买决策中起到日益重要的作用，电商网站提供海量的用户评论，但是个体很难充分利用所有信息。因此，对这些评论进行分类、分析和汇总是很迫切的任务。首次提出一个基于注意力机制和双向LSTM（bi-directional long short-term memory，BLSTM）的模型来判定评论对象的类别，用于评论的分类。模型首先使用BLSTM对词向量形式的评论进行训练；然后根据词性为BLSTM的输出向量赋予相应权重，权重作为先验知识能指导注意力机制的学习；最后使用注意力机制捕捉与类别相关的重要信息用于类别判定。在SemEval数据集上进行了实验，结果表明，模型能有效提高评论对象类别判定的效果，优于其他算法。 |
| 关键词:
用户评论
评论对象类别判定
注意力机制
BLSTM
|
|
| Aspect Category Detection Based on Attention Mechanism and Bi-Directional LSTM |
|
| ZHOU Chenchao1,2, CHEN Qun1,2, LI Zhanhuai1,2, ZHAO Bo3, XU Yongjun3, QIN Yang3 |
|
1. School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China;
2. Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an 710072, China;
3. Unit 95806 of PLA, Beijing 100076, China |
| Abstract: |
| Online reviews play an increasingly important role in users' purchase decisions. E-commerce websites provide massive user reviews, but it is hard for individuals to make full use of the information. Therefore, it is an urgent task to classify, analyze and summarize the massive comments. In this paper, a model based on attention mechanism and bi-directional long short-term memory (BLSTM) is used to identify the categories of these review objects for the classification of the reviews. The model first uses BLSTM to train the review in the form of word vectors; then according to the part-of-speech, the output vectors of the BLSTM are given corresponding weights. The weights as prior knowledge can guide the learning of attention mechanism to enhance the classification accuracy; finally, the attention mechanism is used to capture category-related important features which are used for category determination. Experiments on the SemEval data set show that our model outperforms the state-of-the-art methods on aspect category detection. |
| Key words:
user review
aspect category detection
attention mechanism
bi-directional long short-term memory
classification accuracy
|
|
| 收稿日期: 2018-05-04
修回日期:
|
| DOI: 10.1051/jnwpu/20193730558 |
| 基金项目: 科技部国家重点研发计划（2016YFB1000703）与国家自然科学基金（6173201，61332006）资助 |
| 通讯作者:
Email： |
| 作者简介: 周陈超(1982-),西北工业大学博士研究生,主要从事数据管理和机器学习研究。
|
|
| 相关功能 |
|
|
|
|
| 作者相关文章 |
|
| 周陈超 在本刊中的所有文章 |
| 陈群 在本刊中的所有文章 |
| 李战怀 在本刊中的所有文章 |
| 赵波 在本刊中的所有文章 |
| 胥勇军 在本刊中的所有文章 |
| 秦阳 在本刊中的所有文章 |
|
|
|
|
|
|
|
|
参考文献: |
|
|
[1] ADJEI M T, NOBLE S M, NOBLE C H. The influence of C2C Communications in Online Brand Communities on Customer Purchase Behavior[J]. Journal of the Academy of Marketing Science, 2010, 38(5):634-653
[2] PANG B, LEE L. Opinion Mining and Sentiment Analysis[J]. Foundations and Trends in Information Retrieval, 2008, 2(1/2):1-135
[3] GANU G, NOéMIE ELHADAD, AMéLIE MARIAN. Beyond the Stars:Improving Rating Predictions Using Review Text Content[C]//Proceedings of International Workshop on the Web and Databases, 2009:1-6
[4] LIU B. Opinion Mining and Sentiment Analysis[J]. Synthesis Lectures on Human Language Technologies, 2011, 2(2):459-526
[5] PONTIKI M, GALANIS D, PAVLOPOULOS J, et al. SemEval-2014 Task 4:Aspect Based Sentiment Analysis[C]//Proceedings of International Workshop on Semantic Evaluation, 2014:27-35
[6] KIRITCHENKO S, ZHU X, CHERRY C, et al. NRC-Canada-2014:Detecting Aspects and Sentiment in Customer Reviews[C]//International Workshop on Semantic Evaluation, 2014:437-442
[7] BRYCHCIN T, KONKOL M, STEINBERGER J. UWB:Machine Learning Approach to Aspect-Based Sentiment Analysis[C]//International Workshop on Semantic Evaluation, 2014:817-822
[8] HERCIG T, BRYCHCIN T. UWB at SemEval-2016 Task 5:Aspect Based Sentiment Analysis[C]//International Workshop on Semantic Evaluation, 2016:342-349
[9] RUDER S, GHAFFARI P, BRESLIN J G. INSIGHT-1 at SemEval-2016 Task 5:Deep Learning for Multilingual Aspect-Based Sentiment Analysis[C]//International Workshop on Semantic Evaluation, 2016:330-336
[10] SCHOUTEN K, ONNE V D W, FRASINCAR F, et al. Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-Occurrence Data[J]. IEEE Trans on Cybernetics, 2018, 48(4):1263-1275
[11] MNIH V, HEESS N, GRAVES A, et al. Recurrent Models of Visual Attention[C]//Advances in Neural Information Processing Systems, 2014:2204-2212
[12] WANG Y, HUANG M, ZHU X, et al. Attention-Based LSTM for Aspect-level Sentiment Classification[C]//Conference on Empirical Methods in Natural Language Processing, 2016:606-615
[13] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient Estimation of Word Representations in Vector Space[C]//Interrational Conference on Learning Representations, 2013:1-12
[14] 陈龙, 管子玉, 何金红,等. 情感分类研究进展[J]. 计算机研究与发展, 2017, 54(6):1150-1170 CHEN Long, GUAN Ziyu, HE Jinghong, et al. A Survey on Sentiment Classification[J]. Journal of Computer Research and Development, 2017, 54(6):1150-1170(in Chinese) |
|
|
|
|
|
|
|
