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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (4): 90-98    DOI: 10.11925/infotech.2096-3467.2017.1252
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Extracting Product Features with NodeRank Algorithm
Zhou Lixin, Lin Jie()
School of Economics and Management, Tongji University, Shanghai 200092, China
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Abstract  

[Objective] This paper presents a novel algorithm based on the NLP technique and complex network theory, aiming to extract product features more effectively. [Methods] First, we constructed a weighted bipartite graph with the product features and sentiment words, which described their relationship more clearly and intuitively from network perspective. Then, we proposed the NodeRank algorithm to rank the importance of product features, which improved the precision of feature extraction. [Results] We examined the proposed algorithm with data from jd.com, a popular online shopping site in China. The precision, recall and F-score of the NodeRank algorithm were better than the HAC, TF-IDF and TextRank methods. [Limitations] The computational complexity of our new algorithm needs to be optimized. [Conclusions] The NodeRank algorithm could effectively extract the product features, which supports marketing and other business activities.

Key wordsFeature Extraction      Bipartite Graph      NodeRank Algorithm      Importance Ranking     
Received: 11 December 2017      Published: 11 May 2018
ZTFLH:  TP393  

Cite this article:

Zhou Lixin,Lin Jie. Extracting Product Features with NodeRank Algorithm. Data Analysis and Knowledge Discovery, 2018, 2(4): 90-98.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1252     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I4/90

产品名称 类别 评论数量 清洗后的评论数量
华为G9 Plus铂雅金
4G手机
手机 1 888条 1 366条
排序 特征词 NR 词频 RFF
1 手感 0.02622 65 0.08541
2 外观 0.02141 61 0.08016
3 屏幕 0.01678 39 0.05125
4 电池 0.01614 23 0.03022
5 价格 0.01518 13 0.01708
6 速度 0.01446 59 0.07753
7 质量 0.01238 23 0.03022
8 感觉 0.01191 7 0.02365
9 机身 0.01098 4 0.0092
10 界面 0.01081 6 0.00526
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