Difference between revisions of "Link-PLSA-LDA: A new unsupervised model for topics and influence of blogs"
From Cohen Courses
Jump to navigationJump to searchLine 7: | Line 7: | ||
== Summary == | == Summary == | ||
This paper presents a novel, unsupervised model based of topics and topic specific influences in blogs. It is compared with Link-LDA and performs better. It intends to address two issues at once: topic discovery and modeling topic specific influence of blogs. | This paper presents a novel, unsupervised model based of topics and topic specific influences in blogs. It is compared with Link-LDA and performs better. It intends to address two issues at once: topic discovery and modeling topic specific influence of blogs. | ||
+ | |||
+ | When one blog cites another, this is viewed as a uni-dimensional link. | ||
Not completely generative due to hyperlinked documents being fixed. | Not completely generative due to hyperlinked documents being fixed. | ||
Line 23: | Line 25: | ||
* LDA | * LDA | ||
− | * Link-LDA | + | * [[Cohn_Hofmann]] PHITS dubbed (Link-PLSA) in this paper |
+ | * [[Eroshedva_et_al]] dubbed (Link-LDA) in this paper |
Revision as of 18:50, 30 November 2012
Contents
Citation
Ramesh Nallapti and William Cohen. Link-PLSA-LDA: A new unsupervised model for topics and influence of blogs. In Proc of AAAI 2008.
Online Version
Link-PLSA-LDA: A new unsupervised model for topics and influence of blogs.
Summary
This paper presents a novel, unsupervised model based of topics and topic specific influences in blogs. It is compared with Link-LDA and performs better. It intends to address two issues at once: topic discovery and modeling topic specific influence of blogs.
When one blog cites another, this is viewed as a uni-dimensional link.
Not completely generative due to hyperlinked documents being fixed.
Dataset
Model
Topic Discovery
Modeling Topic Specific Influence of Blogs
Experiments
Study Plan
- LDA
- Cohn_Hofmann PHITS dubbed (Link-PLSA) in this paper
- Eroshedva_et_al dubbed (Link-LDA) in this paper