Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (1): 47-54.doi: 10.6040/j.issn.1672-3961.0.2017.485

• Machine Learning & Data Mining • Previous Articles     Next Articles

Advanced collaborative filtering recommendation model based on sentiment analysis of online review

Chunlin QIAN1,2(),Xingfang ZHANG3,*(),Lihua SUN2   

  1. 1. School of Business Administration, Hohai University, Changzhou 213022, Jiangsu, China
    2. College of Management and Economics, Tianjin University, Tianjin 300072, China
    3. School of Mathematical Sciences, Liaocheng University, Liaocheng 252000, Shandong, China
  • Received:2017-10-08 Online:2019-02-20 Published:2019-03-01
  • Contact: Xingfang ZHANG E-mail:qiancl1997@126.com;sunlh68@tju.edu.cn
  • Supported by:
    国家自然科学基金(11471152)

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Abstract:

Aiming at the uncertainty of users' subject opinions in online Chinese review, a sentiment analysis model was proposed based on uncertainty theory. An individual recommendation algorithm was designed on the basis of the proposed sentiment analysis model. Firstly, the tokenizers of ICTCLAS and IKAnalyzer were used to preprocess online Chinese review to generate characteristic words, and the point mutual information value of characteristic words accounting for the sentiment direction were computed based on sentiment dictionary (HowNet). Then, the sentiment analysis model was established via uncertainty theory of uncertain variable and uncertain set. In addition, the new similarity formula based on the proposed model was used to search the nearest neighbors. Finally, the recommendation lists were given. The experiments were carried out on two real datasets. The results showed that the proposed method could effectively improve the accuracy of recommendation and alleviate the sparse data problem.

Key words: recommendation model, uncertain variable, uncertain set, online review, sentiment analysis

CLC Number: 

  • TP391

Fig.1

Sentiment analysis of polarities"

Table 1

Give examples of six grades of adverbs of degree"

等级1 等级2 等级3 等级4 等级5 等级6
不得了 着实 更加 点点滴滴 半点 不为过
极度 太甚 还要 好生 不堪 开外
酷 不胜 愈加 稍稍 轻度 出头
莫大 特别 这样 有点儿 相对 过
入骨 多多 越是 相当 不怎么 过分

Table 2

Examples of sentiment polarities words"

负向情感词 正向情感词
油腻、虚假、讨厌、脆弱、陈旧 接受、暖心、认可、新鲜、最爱

Table 3

Parameters of datasets"

记录数 用户数 项目数 稀疏度/%
餐馆数据集 560 113 9 171 2 903 97.90
酒店数据集 10 372 1 411 528 98.61
手机数据集 219 732 215 402 1 007 99.90

Table 4

The Precision and F1 of two algorithms"

邻居数量 USR-CF CF
Precision F1 Precision F1
5 0.020 48 0.021 17 0.015 26 0.010 05
15 0.018 02 0.020 52 0.013 45 0.008 84
25 0.016 88 0.018 82 0.012 31 0.007 87
35 0.014 91 0.016 91 0.010 50 0.006 69
45 0.013 74 0.015 07 0.010 66 0.007 11
55 0.011 06 0.013 59 0.009 68 0.006 21

Table 5

The Precision and F1 of two algorithms"

推荐列表 USR-CF FC-Means
Precision F1 Precision F1
5 0.018 19 0.017 78 0.017 40 0.015 66
10 0.018 02 0.020 52 0.018 00 0.017 36
餐馆数据集 15 0.020 70 0.023 83 0.019 04 0.018 63
20 0.021 94 0.024 78 0.020 01 0.019 97
25 0.022 30 0.025 61 0.020 69 0.021 31
5 0.004 81 0.004 87 0.003 81 0.003 97
10 0.004 80 0.005 67 0.003 84 0.004 31
手机数据集 15 0.004 80 0.005 75 0.003 88 0.004 40
20 0.004 82 0.005 33 0.003 90 0.004 40
25 0.004 75 0.005 06 0.003 90 0.004 47

Fig.2

Precision of recommendation results"

Fig.3

F1"

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