Social network bursty topic discovery based on RNN and topic model

Lei SHI; Junping DU; Meiyu LIANG

doi:10.11959/j.issn.1000-436x.2018056

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Social network bursty topic discovery based on RNN and topic model

Correspondences | 更新时间：2024-06-05

- Social network bursty topic discovery based on RNN and topic model
- Journal on Communications Vol. 39, Issue 4, Pages: 189-198(2018)
- 作者机构：
  
  北京邮电大学智能通信软件与多媒体北京市重点实验室，北京 100876
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61320106006);The National Natural Science Foundation of China(61532006);The National Natural Science Foundation of China(61772083)
- DOI：10.11959/j.issn.1000-436x.2018056
  CLC： TP393
- Online First：2018-04，
  
  Published：25 April 2018
- 稿件说明：
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Lei SHI, Junping DU, Meiyu LIANG. Social network bursty topic discovery based on RNN and topic model[J]. Journal on Communications, 2018, 39(4): 189-198.
DOI：

Lei SHI, Junping DU, Meiyu LIANG. Social network bursty topic discovery based on RNN and topic model[J]. Journal on Communications, 2018, 39(4): 189-198. DOI： 10.11959/j.issn.1000-436x.2018056.

摘要

社交网络数据是稀疏和嘈杂的，并伴有大量的无意义话题。传统突发话题发现方法无法解决社交网络短文本稀疏性问题，并需要复杂的后处理过程。为了解决上述问题，提出一种基于循环神经网络（RNN

recurrent neural network）和主题模型的突发话题发现（RTM-SBTD）方法。首先，综合RNN和逆序文档频率（IDF

inverse document frequency）构建权重先验来学习词的关系，同时通过构建词对解决短文本稀疏性问题。其次，模型中引入针板先验（spike and slab）来解耦突发话题分布的稀疏和平滑。最后，引入词的突发性来区分建模普通话题和突发话题，实现突发话题自动发现。实验结果表明与现有的主流突发话题发现方法相比，所提 RTM-SBTD 方法在多种评价指标上优于对比算法。

Abstract

The data is noisy and diverse

with a large number of meaningless topics in social network.The traditional method of bursty topic discovery cannot solve the sparseness problem in social network

and require complicated post-processing.In order to tackle this problem

a bursty topic discovery method based on recurrent neural network and topic model was proposed.Firstly

the weight prior based on RNN and IDF were constructed to learn the relationship between words.At the same time

the word pairs were constructed to solve the sparseness problem.Secondly

the “spike and slab” prior was introduced to decouple the sparsity and smoothness of the bursty topic distribution.Finally

the burstiness of words were leveraged to model the bursty topic and the common topic

and automatically discover the bursty topics.To evaluate the effectiveness of proposed method

the various experiments were conducted.Both qualitative and quantitative evaluations demonstrate that the proposed RTM-SBTD method outperforms favorably against several state-of-the-art methods.

关键词

Keywords

references

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