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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (4): 27-33    DOI: 10.11925/infotech.2096-3467.2019.0765
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Recommending Microblogs Based on Emotion-Weighted Association Rules
Li Tiejun,Yan Duanwu(),Yang Xiongfei
School of Economics & Management, Nanjing University of Science and Technology, Nanjing 210094, China
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Abstract  

[Objective] This study recommends microblogs based on readers’ browsing behaviors, aiming to improve users’ experience with the Weibo services. [Methods] Firstly, we used association rules to analyze users’ behaviors on Sina Weibo and retrieved all frequent 1-item sets for comments. Then, we calculated the emotional intensity of comments, and identified micro-blog posts with emotional intensity higher than the threshold. Finally, we generated a new frequent 1-item set to establish stronger association rules for the final list. [Results] Compared with the benchmark recommendation algorithms, the accuracy, recall and F values of the proposed algorithm were all improved by 10%. [Limitations] The parameters in our experiment were relatively simple, which might not yield the best results. [Conclusions] The proposed method based on emotion-weighted association rules can effectively recommend microblogs.

Key wordsAssociation Rules      Sentiment Analysis      Sentiment Weighting      Microblog Recommendation     
Received: 26 June 2019      Published: 01 June 2020
ZTFLH:  TP391  
Corresponding Authors: Yan Duanwu     E-mail: yanwu123@sina.com

Cite this article:

Li Tiejun,Yan Duanwu,Yang Xiongfei. Recommending Microblogs Based on Emotion-Weighted Association Rules. Data Analysis and Knowledge Discovery, 2020, 4(4): 27-33.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0765     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I4/27

微博用户 微博ID 微博评论情感强度
李荣浩 1 5.6
2 7.5
3 7.8
寧晓言 4 3.8
5 -4.0
6 -7.2
7 4.3
Sentiment Calculation of Microblog
微博频繁1-项集(微博ID) 支持度 情感强度
1 1.1% 6.0(通过)
4 1.7% -4.2(未通过)
Frequent 1-Itemset Filtering Results (Partial)
规则 前项(微博ID) 后项(微博ID) 置信度
1 1,2 7 85.7%
2 13,15 9 79.2%
3 17 113 76.5%
4 1023 117 72.4%
5 88,34 203 70.3%
Strong Association Rules (Partial)
算法 推荐
效果
最小支持度
1.0% 1.5% 2.0% 2.5% 3.0%
情感加权关联规则推荐 准确率 36% 42% 58% 46% 33%
覆盖率 45% 51% 58% 55% 41%
F值 40% 46% 58% 51% 37%
基于关联规则的推荐 准确率 34% 39% 44% 42% 31%
覆盖率 39% 42% 50% 44% 39%
F值 36% 40% 47% 43% 35%
Recommended Results of Collaborative Filtering
算法 准确率 覆盖率 F值
情感加权关联规则推荐 58% 58% 58%
基于LDA的内容相似推荐 47% 51% 49%
Recommended Results with Similar Contents
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