Multi-granularity Microblog User Interest Portrait Construction Based on NWD Integrated Algorithm

Zhang Shu; Mo Zan; Liu Jian-hua; Yang Pei-chen; Liu Hong-wei

doi:10.12052/gdutxb.190129

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Multi-granularity Microblog User Interest Portrait Construction Based on NWD Integrated Algorithm

更新时间：2026-05-27

- Multi-granularity Microblog User Interest Portrait Construction Based on NWD Integrated Algorithm
- Vol. 37, Issue 4, Pages: 42-50(2020)
- 作者机构：
  
  广东工业大学管理学院,广东,广州,510520
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.190129
  CLC： TP391
- Online First：11 July 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
张舒, 莫赞, 柳建华, 杨培琛, 刘洪伟. 基于NWD集成算法的多粒度微博用户兴趣画像构建[J]. 广东工业大学学报, 2020, 37(4): 42-50.

Zhang Shu, Mo Zan, Liu Jian-hua, et al. Multi-granularity Microblog User Interest Portrait Construction Based on NWD Integrated Algorithm[J]. 2020, 37(4): 42-50.
张舒, 莫赞, 柳建华, 杨培琛, 刘洪伟. 基于NWD集成算法的多粒度微博用户兴趣画像构建[J]. 广东工业大学学报, 2020, 37(4): 42-50. DOI： 10.12052/gdutxb.190129.

Zhang Shu, Mo Zan, Liu Jian-hua, et al. Multi-granularity Microblog User Interest Portrait Construction Based on NWD Integrated Algorithm[J]. 2020, 37(4): 42-50. DOI： 10.12052/gdutxb.190129.

摘要

微博文本特殊性的存在使得微博用户兴趣画像难以有效构建。为此

提出了一种集成算法——新词发现−双向长短期记忆网络−梯度提升算法。首先针对微博文本的非正式性

提出了一种基于支持度视角的新词发现(New Word Discovery

NWD)算法

发掘其中大量存在的网络用语以实现更加准确的分词及语义把握; 其次

引入Simhash算法使得微博文本中的“信息过载”现象得到改观; 再次

为改善微博文本的简洁性而引起的特征稀疏问题

采用双向长短期记忆网络(Bidirectional Long Short-term Memory，Bi-LSTM)模型提取博文语义特征; 最后

通过融合微博用户静态特征训练梯度提升(extreme Gradient Boosting，XGBoost)模型

从而有效构建多粒度微博用户兴趣画像。实验结果表明

粗粒度(一级)兴趣标签模型NWD-Bi-LSTM和细粒度(二级)兴趣标签模型NWD-Bi-LSTM-XGBoost的宏平均F1值(Macro-average F1 score

mF1)和受试者工作特征曲线下面积(Area Under ROC Crave

AUC)分别高达83.6%

79.7%和70.4%

63.6%

相对于基准模型

NWD算法的集成使得模型的mF1值和AUC值均能提升3%～5%

其促进作用优于现有的新词发现方法。

Abstract

The special features of microblog text cause difficulties in building microblog user interest portrait. To address the problem

an ensemble algorithm based on NWD-Bi-LSTM-XGBoost is proposed. Firstly

a new word discovery algorithm from the perspective of support is raised to deal with the informality of microblog text

exploring the ubiquitous internet phrases and achieving more accurate word segmentation and semantic understanding. Then

a Simhash algorithm is introduced to mitigate the information overload of microblog text. To improve the feature sparsity caused by microblog text’s conciseness

bidirectional long short-term memory networks are used to extract semantic features. Finally

the XGBoost model is trained by combining the static features of microblog users with the semantic features of the blog text for constructing the multi-granularity microblog user interest portrait efficiently. The experimental results show that the macro-average F1 score and AUC value of coarse-granularity (primary) interest tag model are up to 83.6% and 79.7% and that of fine-granularity (secondary) interest tag model are 70.4% and 63.6%

respectively. Compared with other benchmark models

the macro-average F1 score and AUC value of the models increase by 3%~5% due to ensemble of the NWD algorithm

which is superior to the existing new word discovery methods.

关键词

Keywords

references

QUINTANA R M, HALEY S R, LEVICK A, et al. The persona party: using personas to design for learning at scale[C]// The 2017 CHI Conference Extended Abstracts. Denver, Colorado: ACM Press, 2017: 933-941.

LIN S, ZHENG Z H. A research of a recommender system based on customer behavior modeling by mining association rules [J]. Journal of Guangdong University of Technology, 2018, 35(3): 90-94.

LEI Y M, LIU Y, HUO H. New word discovery based on microblog corpus for network language [J]. Computer Engineering and Design, 2017, 38(3): 789-794.

LIU W T, LIU P Y, LIU W F, et al . New word discovery algorithm based on mutual information and branch entropy [J ] . Application Research of Computers, 2019, 36(5): 1-7.

LI W, GUO K, SHI Y, et al . DWWP: domain-specific new words det ection and word propagation system for sentiment analysis in the tourism domain [J ] . Knowledge-Based Systems, 2018, 146(18): 203-214.

QIN Y B, SUN Y J, WEI X. Microblog user interest mining based on text clustering and interest decay [J]. Application Research of Computers, 2019, 36(5): 28-35.

LIN Y X, XIE X S. User portrait of diversified groups in micro-blog based on social identity theory [J]. Information Studies: Theory Application, 2018, 41(3): 142-148.

KUZMA M, GABRIELA A. Predicting user’s preferences using neural networks and psychology models [J]. Applied Intelligence, 2016, 44(3): 526-538.

DI NG, L H, SUN B, S HI, P. Chinese microblog topic detection through POS-based semantic expansion [J]. Information, 2018, 9(8): 2078-2489.

JANG S Y, WANG L X. Some challenges in clustering analysis [J]. Journal of Guangdong University of Technology, 2014, 31(3): 32-38.

T U, HUANG. Mining microblog user interests based on TextRank with TF-IDF factor [J]. The Journal of China Universities of Posts and Telecommunications, 2016, 23(5): 40-46.

XIONG C W, CAO Y N. Research of microblog user interest mining based on microblog posts [J]. Application Research of Computers, 2018, 35(6): 1619-1623.

MIKE S, KUIDIP K P. Bidirectional recurrent neural networks [J]. IEEE Transactions on Signal Processing, 1997, 45(11): 2673-2681.

CHEN T Q, GUESTRIN C. XGBoost: a scalable tree boosting system[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2016: 785-794.

MANKUS, JAIN A, SARMA D, et al. Detecting near-duplicates for web crawling[C]// Proceedings of the 16th internationalconference on world wide web. Canada: ACM Press, 2007: 141-150.

ZHAO Q L, CAI X D, LI B, et al . Text feature extraction method based on LSTM-Attention neural network [J ] . Modern Electronics Technique, 2018, 41(8): 167-170.

HOCHREITER S, SCHMIDHUBER J. Long short-term memory [J]. Neural Computation, 1997, 9(8): 1735-1780.

FRIEDMAN J H. Greedy function approximation: a gradient boosting machine [J]. The Annals of Statistics, 2001, 29(5): 1189-1232.

AGRAWAL R, SRIKANT R. Fast algorithms for mining association rules[M ] . Readings in database systems (3 rd ed). San Francisco: Morgan Kaufmann Publishers Inc, 1996: 580-592.

SUN X, WANG H F, LI W J. Fast online training with frequency-adaptive learning rates for chinese word segmentation and new word detection[C]// Annual Meeting of the Association for Computational Linguistics. Jeju Island, Korea: ACL, 2012: 253-262.

FU P, LIN Z, YUAN F C. Convolutional neural network and user information based model for microblog topic tracking [J]. Pattern Recognition and Artificial Intelligence, 2017, 30(1): 73-80.

JAMES B, YOSHUA B. Random search for hyper-parameter optimization [J]. Journal of Machine Learning Research, 2012, 13(1): 281-305.

SUN L Y, ZHOU Y D, GUAN X H. A method of discovering new Chinese words from internet based on information propagation [J]. Journal of XI’AN Jiaotong University, 2015, 49(12): 59-64.

DZIKOVSKA M, NIELSEN D, LEACORK C. The joint student response analysis and recognizing textual entailment challenge: making sense of student responses in educational applications [J]. Language Resources and Evaluation, 2016, 50(1): 67-93.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Spatial-temporal Deep Regression Model for Multi-granularity Traffic Flow Prediction

The Cross-twin Physics-informed Neural Network for Wave Equations with Conserved Quantities

Ground Deformation Monitoring and Influencing Factors Analysis in Fuzhou City Based on PSI Technology

SCA-YOLO: Real-time Target Detection Based on Deformable Convolution and Context-aware Attention

A Resource Allocation Optimization Method for Multi-UAV Integrated Sensing, Computing, and Communication Towards Disaster Relief

Related Author

Wen Wen

Liu Ying

Cai Rui-chu

Hao Zhi-feng

Related Institution

School of Computer Science and Technology, Guangdong University of Technology

Engineering School, Shantou University

AI问答

⁰