Please wait a minute...
Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (4): 27-33    DOI: 10.11925/infotech.2096-3467.2019.0765
Current Issue | Archive | Adv Search |
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
Download: PDF (563 KB)   HTML ( 16
Export: BibTeX | EndNote (RIS)      
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:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0765     OR     https://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
[1] 第43次中国互联网络发展状况统计报告[R]. 第43次中国互联网络发展状况统计报告[R]. 北京:中国互联网络信息中心, 2019.
[1] ( The 43rd China Statistical Report on Internet Development[R]. The 43rd China Statistical Report on Internet Development[R]. Beijing: China Internet Network Information Center, 2019.)
[2] Zhang J, Lei Y . Improving Content Recommendation in Social Streams via Interest Model[J]. Studies in Computational Intelligence, 2015,566:57-70.
[3] 孙光明, 王硕, 邹静昭 . 多因素复合度量的协同过滤推荐算法[J]. 计算机应用研究, 2015,32(10):2896-2900.
[3] ( Sun Guangming, Wang Shuo, Zou Jingzhao . Collaborative Filtering Recommendation Algorithm Measured by Compound Multiple Factors[J]. Application Research of Computers, 2015,32(10):2896-2900.)
[4] Chen L, Chen G, Wang F . Recommender Systems Based on User Reviews: The State of the Art[J]. User Modeling and User-Adapted Interaction, 2015,25(2):99-154.
doi: 10.1007/s11257-015-9155-5
[5] Qiu L, Gao S, Cheng W , et al. Aspect-based Latent Factor Model by Integrating Ratings and Reviews for Recommender System[J]. Knowledge-Based Systems, 2016,110:233-243.
doi: 10.1016/j.knosys.2016.07.033
[6] 崔金栋, 杜文强, 关杨 . 基于大数据与LDA融合的微博信息推荐方法研究[J]. 情报科学, 2018,36(9):27-31, 76.
[6] ( Cui Jindong, Du Wenqiang, Guan Yang . Research on Microblog Information Recommendation Method Based on Big Data and LDA Fusion[J]. Information Science, 2018,36(9):27-31,76.)
[7] 孙玉洁, 秦永彬 . 基于LDA模型的多角度个性化微博推荐算法[J]. 计算机工程, 2017,43(4):177-182.
doi: 10.3969/j.issn.1000-3428.2017.04.030
[7] ( Sun Yujie, Qin Yongbin . Multi-angle Personalized Microblog Recommendation Algorithm Based on LDA Model[J]. Computer Engineering, 2017,43(4):177-182.)
doi: 10.3969/j.issn.1000-3428.2017.04.030
[8] 刘慧婷, 程雷, 郭孝雪 , 等. 实时个性化微博推荐系统[J]. 计算机科学, 2018,45(9):253-259, 265.
[8] ( Liu Huiting, Cheng Lei, Guo Xiaoxue , et al. Real-time Personalized Micro-Blog Recommendation System[J]. Computer Science, 2018,45(9):253-259, 265.)
[9] Lemire D, Maclachlan A. Slope One Predictors for Online Rating-Based Collaborative Filtering[C]// Proceedings of the 2005 SIAM Data Mining Conference (SDM’05), Newport Beach, California, USA. 2005: 21-23.
[10] 蔡淑琴, 袁乾, 周鹏 , 等. 基于信息传播理论的微博协同过滤推荐模型[J]. 系统工程理论与实践, 2015,35(5):1267-1275.
[10] ( Cai Shuqin, Yuan Qian, Zhou Peng , et al. Collaborative Filtering Recommendation Model in Micro-Blogging Website Based on Information Diffusion Theory[J]. System Engineering-Theory & Practice, 2015,35(5):1267-1275.)
[11] 马宏伟, 张光卫, 李鹏 . 协同过滤推荐算法综述[J]. 小型微型计算机系统, 2009,30(7):1282-1288.
[11] ( Ma Hongwei, Zhang Guangwei, Li Peng . Survey of Collaborative Filtering Algorithm[J]. Journal of Chinese Computer Systems, 2009,30(7):1282-1288.)
[12] 张乐, 闫强, 吕学强 . 面向短文本的情感折射模型[J]. 情报学报, 2017,36(2):180-189.
[12] ( Zhang Le, Yan Qiang, Lyu Xueqiang . Short Text-Oriented Sentiment Refraction Model[J]. Journal of the China Society for Scientific and Technical Information, 2017,36(2):180-189.)
[13] 张向阳, 那日萨, 孙娜 . 基于有向网络的在线评论情感倾向性分类[J]. 情报科学, 2016,34(11):66-69.
[13] ( Zhang Xiangyang, Na Risa, Sun Na . Emotional Classification for Online Reviews Based on Directed Network[J]. Information Science, 2016,34(11):66-69.)
[14] 唐慧丰, 谭松波, 程学旗 . 基于监督学习的中文情感分类技术比较研究[J]. 中文信息学报, 2007,21(6):88-94.
[14] ( Tang Huifeng, Tan Songbo, Cheng Xueqi . Research on Sentiment Classification of Chinese Reviews Based on Supervised Machine Learning Techniques[J]. Journal of Chinese Information Processing, 2007,21(6):88-94.)
[15] Dasgupta S, Ng V. Mine the Easy, Classify the Hard: A Semi-Supervised Approach to Automatic Sentiment Classification[C]// Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009: 701-709.
[16] 李慧, 柴亚青 . 基于卷积神经网络的细粒度情感分析方法[J]. 数据分析与知识发现, 2019,3(1):95-103.
[16] ( Li Hui, Chai Yaqing . Fine-Grained Sentiment Analysis Based on Convolutional Neural Network[J]. Data Analysis and Knowledge Discovery, 2019,3(1):95-103.)
[17] 周咏梅, 杨佳能, 阳爱民 . 面向文本情感分析的中文情感词典构建方法[J]. 山东大学学报:工学版, 2013,43(6):31-37.
[17] ( Zhou Yongmei, Yang Jianeng, Yang Aiming . A Method on Building Chinese Sentiment Lexicon for Text Sentiment Analysis[J]. Journal of Shandong University: Engineering Science, 2013,43(6):31-37.)
[18] 熊德兰, 程菊明, 田胜利 . 基于HowNet的句子褒贬倾向性研究[J]. 计算机工程与应用, 2008,44(22):143-145.
doi: 10.3778/j.issn.1002-8331.2008.22.042
[18] ( Xiong Delan, Cheng Juming, Tian Shengli . Sentence Orientation Research Based on HowNet[J]. Computer Engineering and Applications, 2008,44(22):143-145.)
doi: 10.3778/j.issn.1002-8331.2008.22.042
[19] Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules in Large Databases[C]// Proceedings of the 20th International Conference on Very Large Data Bases. IEEE, 1994,1215:487-499.
[20] 余以胜, 徐剑彬, 刘鑫艳 . 基于社群挖掘的用户个性化信息推荐方法研究[J]. 情报学报, 2017,36(10):1093-1098.
[20] ( Yu Yisheng, Xu Jianbin, Liu Xinyan . Research on Personalized Information Recommendation Based on Community Structure Mining[J]. Journal of the China Society for Scientific and Technical Information, 2017,36(10):1093-1098.)
[21] 包志强, 宋静霞 . 结合关联规则填充的协同过滤改进算法[J]. 现代电子技术, 2019,42(3):78-81, 86.
[21] ( Bao Zhiqiang, Song Jingxia . Improved Collaborative Filtering Recommendation Algorithm Based on Association Rules Filling[J]. Modern Electronics Technique, 2019,42(3):78-81, 86.)
[1] Xu Yuemei, Wang Zihou, Wu Zixin. Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model[J]. 数据分析与知识发现, 2021, 5(7): 126-138.
[2] Zhong Jiawa,Liu Wei,Wang Sili,Yang Heng. Review of Methods and Applications of Text Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(6): 1-13.
[3] Liu Tong,Liu Chen,Ni Weijian. A Semi-Supervised Sentiment Analysis Method for Chinese Based on Multi-Level Data Augmentation[J]. 数据分析与知识发现, 2021, 5(5): 51-58.
[4] Wang Yuzhu,Xie Jun,Chen Bo,Xu Xinying. Multi-modal Sentiment Analysis Based on Cross-modal Context-aware Attention[J]. 数据分析与知识发现, 2021, 5(4): 49-59.
[5] Li Feifei,Wu Fan,Wang Zhongqing. Sentiment Analysis with Reviewer Types and Generative Adversarial Network[J]. 数据分析与知识发现, 2021, 5(4): 72-79.
[6] Chang Chengyang,Wang Xiaodong,Zhang Shenglei. Polarity Analysis of Dynamic Political Sentiments from Tweets with Deep Learning Method[J]. 数据分析与知识发现, 2021, 5(3): 121-131.
[7] Zhang Mengyao, Zhu Guangli, Zhang Shunxiang, Zhang Biao. Grouping Microblog Users of Trending Topics Based on Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(2): 43-49.
[8] Han Pu, Zhang Wei, Zhang Zhanpeng, Wang Yuxin, Fang Haoyu. Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel[J]. 数据分析与知识发现, 2021, 5(11): 68-79.
[9] Lv Huakui,Liu Zhenghao,Qian Yuxing,Hong Xudong. Relationship Between Financial News and Stock Market Fluctuations[J]. 数据分析与知识发现, 2021, 5(1): 99-111.
[10] Xu Hongxia,Yu Qianqian,Qian Li. Studying Content Interaction Data with Topic Model and Sentiment Analysis[J]. 数据分析与知识发现, 2020, 4(7): 110-117.
[11] Jiang Lin,Zhang Qilin. Research on Academic Evaluation Based on Fine-Grain Citation Sentimental Quantification[J]. 数据分析与知识发现, 2020, 4(6): 129-138.
[12] Shi Lei,Wang Yi,Cheng Ying,Wei Ruibin. Review of Attention Mechanism in Natural Language Processing[J]. 数据分析与知识发现, 2020, 4(5): 1-14.
[13] Shen Zhuo,Li Yan. Mining User Reviews with PreLM-FT Fine-Grain Sentiment Analysis[J]. 数据分析与知识发现, 2020, 4(4): 63-71.
[14] Xue Fuliang,Liu Lifang. Fine-Grained Sentiment Analysis with CRF and ATAE-LSTM[J]. 数据分析与知识发现, 2020, 4(2/3): 207-213.
[15] Zhang Yipeng,Ma Jingdong. Analyzing Sentiments and Dissemination of Misinformation on Public Health Emergency[J]. 数据分析与知识发现, 2020, 4(12): 45-54.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn