Difference between revisions of "Yano et al ICWSM 2010. What’s Worthy of Comment? Content and Comment Volume in Political Blogs"
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| + | Apart from step 2(c), the model is identical to a (smoothed) LDA. <math>m_{d,k}</math> is the mixture weight, and <math>v_d</math> is chosen from a mixture of distributions. | ||
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== Experimental Result == | == Experimental Result == | ||
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== Related Papers == | == Related Papers == | ||
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| + | The Topic-Poisson model is essentially a type of supervised or annotated LDA as defined in [[RelatedPaper::Blei and McAuliffe 2008 Supervised topic models|Blei and McAuliffe (2008)]] and [[RelatedPaper::Ramage 2009 Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora|Ramage et al. (2009)]]. | ||
== Study Plan == | == Study Plan == | ||
Revision as of 14:02, 30 September 2012
Contents
Citation
Tae Yano and Noah A. Smith. What’s Worthy of Comment? Content and Comment Volume in Political Blogs. In Proc of ICWSM 2010.
Online Version
What’s Worthy of Comment? Content and Comment Volume in Political Blogs.
Summary
This Paper describes a topic model based approach in modeling the relationship between the text content of a political blog post and the comment volume (i.e. the total amount of response) that a post will receive.
Brief description of the method
The author's propose a generative model, called the Topic-Poisson model, which proceeds as follows. The number of topic is fixed in advance.
Apart from step 2(c), the model is identical to a (smoothed) LDA. is the mixture weight, and is chosen from a mixture of distributions.
Experimental Result
Task: Predict whether a blog post will have higher volume than the average seen in training data (Note that they are NOT predicting the absolute number of words in the comments)
The authors use a subset of the Yano & Smith blog dataset; data from 2 blogs, Matthew Yglesias (denoted MY) and Red State (denoted RS) were used.
The compared models were:
- Naive Bayes
- Regression: linear regression with elastic net regularization
- Topic Poisson
- Topic Negative Binomial
- CommentLDA
Discussion
Related Papers
The Topic-Poisson model is essentially a type of supervised or annotated LDA as defined in Blei and McAuliffe (2008) and Ramage et al. (2009).
