[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.
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.
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.
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.
Bafna K, Toshniwal D . Feature Based Summarization of Customers’ Reviews of Online Products[J]. Procedia Computer Science, 2013,22:142-151.
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.
Hu Y, Boyd-Graber J, Satinoff B , et al. Interactive Topic Modeling[J]. Machine Learning, 2014,95(3):423-469.
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.
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.
( 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.)
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.
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.
( 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.)
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.
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.
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.
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.
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.
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.
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.
( 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.)
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.