Topic Models: A Tutorial with R

被引：7

作者：

Richardson, G. Manning ^{[1
]}

Bowers, Janet ^{[2
]}

Woodill, A. John ^{[3
]}

Barr, Joseph R. ^{[2
]}

Gawron, Jean Mark ^{[4
]}

Levine, Richard A. ^{[2
]}

机构：

[1] San Diego State Univ, Computat Sci Res Ctr, San Diego, CA 92182 USA

[2] San Diego State Univ, Dept Math & Stat, San Diego, CA 92182 USA

[3] San Diego State Univ, Dept Econ, San Diego, CA 92182 USA

[4] San Diego State Univ, Dept Linguist & Asian Middle Eastern Languages, San Diego, CA 92182 USA

来源：

INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING | 2014年 / 8卷 / 01期

关键词：

Probabilistic topic models; latent semantic analysis; microblogging; twitter;

D O I：

10.1142/S1793351X14500044

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This tutorial presents topic models for organizing and comparing documents. The technique and corresponding discussion focuses on analysis of short text documents, particularly micro-blogs. However, the base topic model and R implementation are generally applicable to text analytics of document databases.

引用

页码：85 / 98

页数：14

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