期刊论文

Building Product Feature Dictionary with Large-scale Review Data

中文

数据分析与知识发现

2096-3467

2018

2

1

41-50

10.11925/infotech.2096-3467.2017.0717

国家自然科学基金项目(71571084)/国家自然科学基金项目(71271099)

本校为第一机构

【目的】基于大规模评论数据,提出一种产品特征词典的构建方法,以提升识别产品特征的查准率和查全率。【方法】在人工标注的基础上,基于同义词林的扩展,以及大规模评论文本的词向量训练,计算词语的语义相似度和相关性,进行特征的识别与归并,进而形成产品特征词典。【结果】本文选取手机、相机、图书三类产品的评论数据进行实验,平均查准率和平均查全率分别为0.774和0.855。结果显示,该方法具有一定的普适性。【局限】标注及验证需大量人工参与,自动化程度不够;没有考虑评论中的隐含特征。【结论】通过与已有研究比较,验证了本文提出方法的有效性,特别在查全率上具有显著的提升。

[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 preci...