Implicit Feature Identification in Product Reviews
Zhang Li1, Xu Xin2
1 Department of Computer Science and Technology, Nanjing University, Nanjing 210093, China;
2 Department of Information Science, Bussiness School, East China Normal University, Shanghai 200241, China
[Objective] Opinion mining in product areas draws more and more attention and becomes a hot research topic. The outcome of opinion mining can be used widely just like harmful information filtering, society opinion analysis, user consumption guidance and product improvement and so on. Implicit feature identification plays an important role because implicit features are common in network comments and the identification of them is difficult. [Methods] This paper uses the comments against a certain automobile brand which only have the explicit features to get refined multi-POS opinions and generate opinion clusters by using Synonyms Forests. Meanwhile identify opinions based on field common phrases. Dictionary in the form of {Feature, Opinion, Weight} is generated by using features and opinions, and the weight is calculated. Then deploy explicitly multi-strategy property extraction algorithm based on a dictionary and consider similarity of the opinions in unmatched comments including implicit features and dictionary. [Results] Implicit features can be extracted effectively and the F-value is 75.55% which reaches the good result of the identification of implicit features. [Limitations] Data labeling is a time-consuming job. [Conclusions] Experiment of the new algorithm shows positive result and has some practical value.
[1] Kim S M, Hovy E. Determining the Sentiment of Opinions [C]. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING-04). 2004: 1367-1373.
[2] Hai Z, Chang K, Kim J J. Implicit Feature Identification via Co-occurrence Association Rule Mining [C]. In: Proceedings of the 12th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing'11). 2011:393-404.
[3] Liu B, Hu M, Cheng J. Opinion Observer: Analyzing and Comparing Opinions on the Web [C]. In: Proceedings of the 14th International Conference on World Wide Web. 2005: 342-351.
[4] Zhuang L, Jing F, Zhu X Y. Movie Review Mining and Summarization [C]. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM'06). 2006: 43-50.
[5] Su Q, Xiang K, Wang H, et al. Using Pointwise Mutual Information to Identify Implicit Features in Customer Reviews [C]. In: Proceedings of the 21st International Conference on Computer Processing of Oriental Languages: Beyond the Orient: the Research Challenges Ahead (ICCPOL'06). 2006: 22-30.
[6] Su Q, Xu X, Guo H, et al. Hidden Sentiment Association in Chinese Web Opinion Mining [C]. In: Proceedings of the 17th International Conference on World Wide Web. 2008: 959-968.
[7] Zhang Y, Zhu W. Extracting Implicit Features in Online Customer Reviews for Opinion Mining [C]. In: Proceedings of the 22nd International Conference on World Wide Web. 2013: 103-104.
[8] 仇光, 郑淼, 张晖, 等. 基于正则化主题建模的隐式产品属性抽取[J]. 浙江大学学报: 工学版, 2011, 45(2): 288-294. (Qiu Guang, Zheng Miao, Zhang Hui, et al. Implicit Product Feature Extraction Through Regularized Topic Modeling [J]. Journal of Zhejiang University: Engineering Science, 2011, 45(2): 288-294.)
[9] Poria S, Cambria E, Gelbukh A, et al. A Rule-based Approach to Aspect Extraction from Product Reviews [C]. In: Proceedings of the 2nd Workshop on Natural Language Processing for Social Media (SocialNLP). 2014: 28-37.
[10] Xu H, Zhang F, Wang W. Implicit Feature Identification in Chinese Reviews Using Explicit Topic Mining Model [J]. Knowledge-Based Systems, 2015, 76: 166-175.
[11] 哈工大社会计算与信息检索研究中心同义词词林扩展版[EB/OL]. [2015-06-01]. http://www.ltp-cloud.com/download/. (HIT-SCIR Synonym Word Forest [EB/OL]. [2015-06-01]. http://www.ltp-cloud.com/download/.)
[12] 哈工大语言技术平台LTP [EB/OL]. [2015-06-01]. http:// www.ltp-cloud.com/. (Language Technology Platform [EB/OL]. http://www.ltp-cloud.com/.)
[13] Turney P D. Mining the Web for Synonyms: PMI-IR Versus LSA on TOEFL [C]. In: Proceedings of the 12th European Conference on Machine Learning. Springer-Verlag London, 2001: 491-502.
