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Fine-Grained opinion extraction from Chinese Car Reviews with an Integrated Strategy

Journal of Shanghai Jiaotong university(Science) 2018年第5期23卷 620-626页

作者： WANG Yinglin WANG Ming School of Information Management and Engineering Shanghai University of Finance and EconomicsShanghai 200433China

With rapid development of E-commerce, a large amount of data including reviews about different types of products can be accessed within short time. On top of this, opinion mining is becoming increasingly effective to extract valuable information for product design, improvement and brand marketing, especially with fine-grained opinion mining. However, limited by the unstructured and causal expression of opinions, one cannot extract valuable information conveniently. In this paper, we propose an integrated strategy to automatically extract feature-based information, with which one can easily acquire detailed opinion about certain *** adaptation to the reviews' characteristics, our strategy is made up of a multi-label classification(MLC) for reviews, a binary classification(BC) for sentences and a sentence-level sequence labelling with a deep learning method. During experiment, our approach achieves 82% accuracy in the final sequence labelling task under the setting of a 20-fold cross validation. In addition, the strategy can be expediently employed in other reviews as long as there is an according amount of labelled data for startup.

关键词： opinion extraction multi-label classification (MLC) binary classification (BC) sequence labelling recurrent neural network (RNN)

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A High-Performance extraction Method for Public opinion on Internet

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Wuhan University Journal of Natural Sciences 2007年第5期12卷 902-906页

作者： LI Yanling DAI Guanzhong ZHU Yehang QIN Sen School of Automatic Control Northwestern Polytechnical University Xi＇an 710072 Shaanxi China Department of Computer and Command Automation The Secondary Artillery Engineering College Xi＇an 710025 Shaanxi China

Aiming at the importance of the analysis for public opinion on Internet, the authors propose a high-performance extraction method for public opinion. In this method, the space model for classification is adopted to describe the relationship between words and categories. The combined feature selection method is used to remove noisy words from the original feature space effectively. Then the category weight of words is calculated by the improved formula combining the frequency of words and distribution of words. Finally, the class weights of the not-categorized documents based on the category weight of words are obtained for realizing opinion extraction. Experiment results show that the method has comparatively high classification and good stability.

关键词： opinion extraction text categorization class spacemodel feature selection

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Structured Web Review extraction and opinion Summarization

Structured Web Review Extraction and Opinion Summarization

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第四届全国信息检索与内容安全学术会议

作者： Suke Li~(1,2) Zhong Chen~(1,+) Liyong Tang~1 Jianbin Hu~1 ~1 Institute of Software,School of Electronics Engineering and Computer Science,Peking University Key Laboratory of High Confidence Software Technologies(Peking University),Ministry of Education,Beijing 100871,China ~2School of Software and Microelectronics,Peking University

This paper focuses on structured Web review extraction and opinion *** opinion extraction framework and a feature-opinion based opinion summarization algorithm(FOOSA)are *** related Web pages are first clustered according to their link and title *** reviews are extracted using tag patterns which are generated through one instance learning *** extracted reviews are filtered basically according to some rules to remove meaningless *** last,all reviews are segmented into feature-opinion sentences for continual *** opinion summarization,FOOSA uses feature-opinion pairs as keys to construct feature-opinion(FO) *** we use inter bucket reducing method based on information entropy to remove duplicated sentences in different *** also carries out intra bucket clustering to select representative sentences from every bucket. FOOSA builds positive and negative text opinion summaries through our ranking method.A structured review extraction and opinion summarization system is implemented based on our *** system provides not only text opinion summarization,but also Web based graphical overall sentiment *** show our review extraction and opinion summarization approaches are effective and promising.

关键词： opinion extraction opinion Summarization opinion Mining

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