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| Building Product Feature Dictionary with Large-scale Review Data |
Li Weiqing1,2, Wang Weijun2( ) |
1(School of Information Management, Central China Normal University, Wuhan 430079, China)
2(Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China) |
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Abstract [Objective] This paper proposes a method to build product feature dictionary based on large scale review data, aiming to improve its precision and recall. [Methods] First, we constructed a seed dictionary by manually labeling and extending the synonym forest. Then we trained the word vector with large scale product reviews to calculate the semantic similarity and relevance of words. Finally, we identified and categorized the product features to construct the dictionary. [Results] We chose product reviews on mobile-phones, cameras and books to examine the proposed model, which had average precision and recall of 0.774 and 0.855. [Limitations] The proposed method required a great deal of human participation at the marking and verification stages, while it did not consider the implied features of product reviews. [Conclusions] The proposed method could effectively build feature dictionary with better recall.
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Received: 21 July 2017
Published: 05 February 2018
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