Fine-Grained Opinion Extraction from Chinese Car Reviews with an Integrated Strategy  

作　　者：WANG Yinglin WANG Ming 王英林;王明(School of Information Management and Engineering,Shanghai University of Finance and Economics)

机构地区：School of Information Management and Engineering,Shanghai University of Finance and Economics,Shanghai 200433,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2018年第5期620-626,共7页上海交通大学学报（英文版）

基　　金：the National Natural Science Foundation of China(No.61375053)

摘　　要：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 products.For 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.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 products. For 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） 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]