Multiple Relational Topic Modeling for Noisy Short Texts

Author:

Liu Zheng1,Liu Chiyu1,Xia Bin1,Li Tao1

Affiliation:

1. Jiangsu Key Laboratory of Big Data Security & Intelligent Processing School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, P. R. China

Abstract

Understanding contents in social networks by inferring high-quality latent topics from short texts is a significant task in social analysis, which is challenging because social network contents are usually extremely short, noisy and full of informal vocabularies. Due to the lack of sufficient word co-occurrence instances, well-known topic modeling methods such as LDA and LSA cannot uncover high-quality topic structures. Existing research works seek to pool short texts from social networks into pseudo documents or utilize the explicit relations among these short texts such as hashtags in tweets to make classic topic modeling methods work. In this paper, we explore this problem by proposing a topic model for noisy short texts with multiple relations called MRTM (Multiple Relational Topic Modeling). MRTM exploits both explicit and implicit relations by introducing a document-attribute distribution and a two-step random sampling strategy. Extensive experiments, compared with the state-of-the-art topic modeling approaches, demonstrate that MRTM can alleviate the word co-occurrence sparsity and uncover high-quality latent topics from noisy short texts.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Software

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