Building a TIN-LDA Model for Mining Microblog User's Interest

被引:16
作者
Zheng, Wei [1 ]
Ge, Bin [2 ]
Wang, Chishe [3 ]
机构
[1] Anhui Univ Sci & Technol, Huainan 232000, Peoples R China
[2] Anhui Univ Sci & Technol, Dept Network & Informat Secur, Huainan 232000, Peoples R China
[3] Jingling Inst Technol, Coll Internet & Commun, Nanjing 211100, Jiangsu, Peoples R China
来源
IEEE ACCESS | 2019年 / 7卷
关键词
Dynamic interest hierarchical orientation; LDA topic model; interest topics and keywords; TIN-LDA model; interest attributes; SENTIMENT ANALYSIS; RECOMMENDATION;
D O I
10.1109/ACCESS.2019.2897910
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A latent Dirichlet allocation (LDA) model is a common method for mining the interest of microblog users. But the LDA model does not reflect the hierarchical and dynamic trend of microblog users' interest. As a result, this paper combines with the timeliness and interactivity of microblog, to judge the hierarchical orientation and dynamic interest trend orientation of users' interest. And based on the dynamic interest hierarchical orientation, the three-layers interest network (TIN-LDA) model is constructed to mine the interest of microblog users. In addition, this model expands interest attributes. Interest attributes include contents, contents marked with special symbols, forwarding contents, along with the authentication user name and authentication information. Bringing the interest attributes into users' interest analysis so as to improve the accuracy of mining microblog users' interest keywords and topics. Topic quality assessment and perplexity evaluation were used to verify the effectiveness of the TIN-LDA model in mining the interest of microblog users.
引用
收藏
页码:21795 / 21806
页数:12
相关论文
共 30 条
[1]   A SURVEY OF TECHNIQUES FOR EVENT DETECTION IN TWITTER [J].
Atefeh, Farzindar ;
Khreich, Wael .
COMPUTATIONAL INTELLIGENCE, 2015, 31 (01) :132-164
[2]   Latent Dirichlet allocation [J].
Blei, DM ;
Ng, AY ;
Jordan, MI .
JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) :993-1022
[3]   Affective Algorithm for Controlling Emotional Fluctuation of Artificial Investors in Stock Markets [J].
Cabrera, Daniel ;
Cubillos, Claudio ;
Cubillos, Alonso ;
Urra, Enrique ;
Mellado, Rafael .
IEEE ACCESS, 2018, 6 :7610-7624
[4]   Affective Computing and Sentiment Analysis [J].
Cambria, Erik .
IEEE INTELLIGENT SYSTEMS, 2016, 31 (02) :102-107
[5]  
Chen Y., 2017, COMPUT TECHNOL DEV, V27, P173
[6]   Natural language processing [J].
Chowdhury, GG .
ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY, 2003, 37 :51-89
[7]   Using time topic modeling for semantics-based dynamic research interest finding [J].
Daud, Ali .
KNOWLEDGE-BASED SYSTEMS, 2012, 26 :154-163
[8]  
Ding Y., 2005, P 14 ACM INT C INF K, P485, DOI DOI 10.1145/1099554.1099689
[9]   Like It or Not: A Survey of Twitter Sentiment Analysis Methods [J].
Giachanou, Anastasia ;
Crestani, Fabio .
ACM COMPUTING SURVEYS, 2016, 49 (02)
[10]   Deep Learning for an Effective Nonorthogonal Multiple Access Scheme [J].
Gui, Guan ;
Huang, Hongji ;
Song, Yiwei ;
Sari, Hikmet .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (09) :8440-8450