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New Technology of Library and Information Service  2014, Vol. 30 Issue (12): 44-50    DOI: 10.11925/infotech.1003-3513.2014.12.06
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Using Dependency Parsing Pattern to Extract Product Feature Tags
Nie Hui, Du Jiazhong
School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China
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[Objective] The method of association recognition for features and the relevant opinions is investigated in order to extract features tags and summarize users' generated online reviews, which is helpful for Web users to access useful information effectively, especially when online information normally varies greatly in quality. [Methods] The dependency parsing is employed to obtain the extraction templates, the template library is constructed after the processes of classifying, filtering and generalization. In terms of the templates and the corresponding external lexicons, feature tags are extracted and sifted out according to the filtering rules. [Results] The experiment results indicate that the method outperforms the similar one which is only based on templates filtration or generalization. The performance of F-measure achieves 56.5% and the accuracy could reach 65% by adjusting the corresponding parameters. [Limitations] The filtering strategy for improving the quality of review data is not conducted in the research. Building feature lexicon automatically and adding more syntactic relations need to consider to extend the library of templates and make improvement of extraction accuracy further. [Conclusions] The better performance can be achieved by finding the most appropriate values for the template-specific parameters, such as the length of template, or by adopting an effective filtering window strategy to detect the noise templates.

Key wordsReview mining      Tags extraction      Dependency parsing analysis     
Received: 23 June 2014      Published: 20 January 2015
:  TP391  

Cite this article:

Nie Hui, Du Jiazhong. Using Dependency Parsing Pattern to Extract Product Feature Tags. New Technology of Library and Information Service, 2014, 30(12): 44-50.

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