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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (2/3): 207-213    DOI: 10.11925/infotech.2096-3467.2019.0678
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Fine-Grained Sentiment Analysis with CRF and ATAE-LSTM
Xue Fuliang(),Liu Lifang
Business School, Tianjin University of Finance & Economics, Tianjin 300222, China
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Abstract  

[Objective] This paper tries to extract product attributes, aiming to cluster these words and analyze user’s sentiments.[Methods] Firstly, we identified the attributes of products with CRF technique. Then, we analyzed the sentiment of extracted terms with attention-based LSTM. Finally, we clustered these terms into appropriate categories with the help of Word2Vec and conducted fine-grained sentiment analysis of the products.[Results] The F1 values of term extraction and sentiment analysis were 0.76 and 0.78.[Limitations] We only retrieved explicit terms for this study and the sample size needs to be expanded.[Conclusions] The proposed method could effectively explore user’s preference in products.
Key words： CRF      LSTM      Attention Mechanism      Sentiment Analysis      Word2Vec     
Received: 14 June 2019      Published: 26 April 2020
ZTFLH:  TP391  
Corresponding Authors: Xue Fuliang     E-mail: fuliangxue@163.com
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Fuliang Xue
Lifang Liu

Cite this article:

Xue Fuliang,Liu Lifang. Fine-Grained Sentiment Analysis with CRF and ATAE-LSTM. Data Analysis and Knowledge Discovery, 2020, 4(2/3): 207-213.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0678     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I2/3/207

Research Framework
ATAE-LSTM Structure
Skip-gram Model
评论 Pos Tag 评论 Pos Tag
运行/ v B 也/ d O
速度/ n I 很/ d O
快/ a O 耐用/ a O
,/ x O 。 x O
电池/ n B
Examples of Attribute Word Tags
The Trend of Euclidean Distance Under Different Clustering Number K
Results of Attribute Word Clustering
实验 P R F1
基于CRF抽取属性词
基于关联规则抽取属性词		 0.84
0.48		 0.70
0.14		 0.76
0.21
基于ATAE-LSTM属性情感分析
基于LSTM属性情感分析		 0.78
0.71		 0.81
0.79		 0.78
0.73
Experimental Result
属性面 属性词 正面情感 中性情感 负面情感
设计 设计 75% 0 25%
外形与功能 信号 4% 96% 0
相机 75% 0 25%
外形 89% 0 11%
摄像头 9% 0 91%
速度 充电速度 100% 0 0
系统速度 100% 0 0
Emotional Indicators of Some Attribute Surfaces
[1] Cheng Z, Ding Y, He X , et al. A^ 3NCF: An Adaptive Aspect Attention Model for Rating Prediction [C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018: 3748-3754.
[2] Wang N, Wang H, Jia Y , et al. Explainable Recommendation via Multi-Task Learning in Opinionated Text Data [C]// Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 2018: 165-174.
[3] Hu M, Liu B . Mining and Summarizing Customer Reviews [C]// Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2004: 168-177.
[4] Bafna K, Toshniwal D . Feature Based Summarization of Customers’ Reviews of Online Products[J]. Procedia Computer Science, 2013,22:142-151.
[5] Chen Z, Liu B . Mining Topics in Documents: Standing on the Shoulders of Big Data [C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2014: 1116-1125.
[6] Hu Y, Boyd-Graber J, Satinoff B , et al. Interactive Topic Modeling[J]. Machine Learning, 2014,95(3):423-469.
[7] Lafierty J D, McCallum A, Pereira F C N . Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data [C]// Proceedings of the 18th International Conference on Machine Learning. Burlington, Massachusetts, USA: Morgan Kaufmann Publishers, 2001: 282-289.
[8] Huang S, Liu X, Peng X , et al. Fine-grained Product Features Extraction and Categorization in Reviews Opinion Mining [C]// Proceedings of the 12th International Conference on Data Mining Workshops. IEEE, 2012: 680-686.
[9] 郑丽娟, 王洪伟 . 基于情感本体的在线评论情感极性及强度分析:以手机为例[J]. 管理工程学报, 2017,31(2):47-54.
[9] ( Zheng Lijuan, Wang Hongwei . Sentimental Polarity and Strength of Online Cellphone Reviews Based on Sentiment Ontology[J]. Journal of Industrial Engineering and Engineering Management, 2017,31(2):47-54.)
[10] 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-Internet & Web Information Systems, 2017,20(2):135-154.
[11] Akhtar M S, Gupta D, Ekbal A , et al. Feature Selection and Ensemble Construction: A Two-Step Method for Aspect Based Sentiment Analysis[J]. Knowledge-Based Systems, 2017,125:116-135.
[12] 李阳辉, 谢明, 易阳 . 基于深度学习的社交网络平台细粒度情感分析[J]. 计算机应用研究, 2017,34(3):743-747.
[12] ( Li Yanghui, Xie Ming, Yi Yang . Fine-grained Sentiment Analysis for Social Network Platform Based on Deep-learning Model[J]. Application Research of Computers, 2017,34(3):743-747.)
[13] Wu H, Gu Y, Sun S , et al. Aspect-based Opinion Summarization with Convolutional Neural Networks [C]// Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016: 3157-3163.
[14] Xu L, Lin J, Wang L , et al. Deep Convolutional Neural Network Based Approach for Aspect-Based Sentiment Analysis[J]. Advanced Science and Technology Letters, 2017,143:199-204.
[15] Toh Z, Su J . NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis Using Neural Network Features [C]// Proceedings of the 10th International Workshop on Semantic Evaluation. 2016: 282-288.
[16] Peng H, Ma Y, Li Y , et al. Learning Multi-Grained Aspect Target Sequence for Chinese Sentiment Analysis[J]. Knowledge-Based Systems, 2018,148:167-176.
[17] Rush A M, Chopra S, Weston J . A Neural Attention Model for Abstractive Sentence Summarization [C]// Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015: 379-389.
[18] Hermann K M, Kocisky T, Grefenstette E , et al. Teaching Machines to Read and Comprehend [C]// Proceedings of the 28th International Conference on Neural Information Processing Systems. 2015: 1693-1701.
[19] Wang Y, Huang M, Zhao L , et al. Attention-Based LSTM for Aspect-Level Sentiment Classification [C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016: 606-615.
[20] 彭敏, 席俊杰, 代心媛 , 等. 基于情感分析和LDA主题模型的协同过滤推荐算法[J]. 中文信息学报, 2017,31(2):194-203.
[20] ( Peng Min, Xi Junjie, Dai Xinyuan , et al. Collaborative Filtering Recommendation Based on Sentiment Analysis and LDA Topic Model[J]. Journal of Chinese Information Processing, 2017,31(2):194-203.)
[21] 李良强, 袁华, 叶开 , 等. 基于在线评论词向量表征的产品属性提取[J]. 系统工程学报, 2018,33(5):687-697.
[21] ( Li Liangqiang, Yuan Hua, Ye Kai , et al. Extraction Product Features from Online Reviews Based on Word-Vector-Representation[J]. Journal of Systems Engineering, 2018,33(5):687-697.)
[22] 王荣洋, 鞠久朋, 李寿山 , 等. 基于CRFs的评价对象抽取特征研究[J]. 中文信息学报, 2012,26(2):56-61.
[22] ( Wang Rongyang, Ju Jiupeng, Li Shoushan , et al. Feature Engineering for CRFs Based Opinion Target Extraction[J]. Journal of Chinese Information Processing, 2012,26(2):56-61.)
[23] Mikolov T, Sutskever I, Chen K , et al. Distributed Representations of Words and Phrases and Their Compositionality [C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013: 3111-3119.
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