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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
Lixin Zhou,Jie Lin()
School of Economics and Management, Tongji University, Shanghai 200092, China
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[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, 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

Cite this article:

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

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