Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (2): 50-59.doi: 10.6040/j.issn.1672-3961.0.2019.403

• Machine Learning & Data Mining • Previous Articles     Next Articles

Identification of the same product feature based on multi-dimension similarity and sentiment word expansion

Longmao HU1,2(),Xuegang HU1,*()   

  1. 1. School of Computer and Information, Hefei University of Technology, Hefei 230601, Anhui, China
    2. Anhui Finance and Trade Vocational College, Hefei 230601, Anhui, China
  • Received:2019-07-17 Online:2020-04-20 Published:2020-04-16
  • Contact: Xuegang HU E-mail:hulongmao@163.com;jsjxhuxg@hfut.edu.cn
  • Supported by:
    国家自然科学基金项目(61673152);安徽省高校自然科学研究重点项目(KJ2017A858)

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Abstract:

Because the existing methods for identifying the same product features were limited by the lack of dictionary coverage or corpus size, an identification method was proposed based on multidimensional similarity and sentiment word expansion. Extracting emotional words of product features through bi-directional long short-term memory and conditional random field (Bi-LSTM-CRF), combining the morpheme similarity, Cilin similarity and term frequency-inverse document frequency (TF-IDF) cosine similarity of product feature words, the same product features were identified by K-medoids clustering algorithm. The experimental results showed that, on mobile and notebook datasets, the maximum adjusted rand index (ARI) reached 0.579 and 0.595 9 respectively, while the minimum entropy reached 0.782 6 and 0.745 7. The proposed method was superior to the adjusted Jaccard similarity combined morpheme, Word2Vec similarity and Word2Vec similarity based on bisecting K-means.

Key words: product feature, sentiment word expansion, Bi-LSTM-CRF, multi-dimension, similarity calculation

CLC Number: 

  • TP391

Fig.1

Structure of Bi-LSTM-CRF"

Fig.2

Basic process of identify the same product feature"

Table 1

Contingency table of clustering and partitioning"

C L1 L2 … Ls Sums
C1 n11 n12 … n1s a1
C2 n21 n22 … n2s a2
⋮ ⋮ ⋮ ⋮ ⋮
Ck nk1 nk2 … nks ak
Sums b1 b2 … bs n

Table 2

Semantic similarity"

方法 斯皮尔曼系数 产品特征词覆盖率/%
同义词林相似度 0.299 3 35.68
调整Jaccard相似度 0.122 3 100
调整Jaccard相似度(扩充情感词) 0.293 3 100
TF-IDF余弦相似度 0.209 9 100
TF-IDF余弦相似度(扩充情感词) 0.434 2 100
Word2Vec相似度 -0.001 3 0.250 3
Word2Vec相似度(扩充评论) 97.83 98.92

Fig.3

Experimental results of α threshold selection"

Table 3

Results of clustering"

项目 语素+TF-IDF余弦相似度(扩充情感词)+同义词林 语素+调整Jaccard相似度(扩充情感词) Word2Vec相似度(扩充评论) Word2Vec相似度(扩充评论，biKMeans聚类)
ARI Entropy ARI Entropy ARI Entropy ARI Entropy
最大 0.579 0 1.402 7 0.541 2 1.506 3 0.150 1 2.606 5 0.123 5 2.443 4
最小 0.223 0 0.782 6 0.200 0 0.877 4 0.044 0 1.975 4 0.062 8 2.010 6
平均 0.361 6 1.081 0 0.330 3 1.168 2 0.091 8 2.244 1 0.087 2 2.242 4
标准差 0.046 4 0.084 2 0.045 4 0.085 7 0.016 0 0.085 0 0.020 4 0.120 6

Fig.4

Maximum ARI value for different number of expanded reviews"

Table 4

Results of clustering for notebook computer data set"

项目 语素+ TF-IDF余弦相似度(扩充情感词)+同义词林 语素+调整Jaccard相似度(扩充情感词) Word2Vec相似度(扩充评论) Word2Vec相似度(扩充评论，biKMeans聚类)
ARI Entropy ARI Entropy ARI Entropy ARI Entropy
最大 0.595 9 1.284 1 0.543 1 1.372 0 0.375 1 1.748 2 0.280 8 1.593 1
最小 0.273 9 0.754 7 0.250 5 0.872 7 0.183 4 1.228 1 0.226 7 1.459 9
平均 0.400 2 1.008 6 0.354 2 1.136 1 0.277 0 1.455 7 0.255 8 1.556 8
标准差 0.040 5 0.075 6 0.038 2 0.071 1 0.028 4 0.068 2 0.018 8 0.038 2
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