DocumentCode
1870489
Title
Persona Analysis with Text Topic Modelling
Author
Dong-mei Yang ; Hui Zheng ; Ji-kun Yan ; Ye Jin
Author_Institution
Science and Technology on Blind Signal Processing Laboratory, Mail Box No.666, Chengdu, China, 610041
fYear
2012
fDate
3-5 March 2012
Firstpage
1559
Lastpage
1563
Abstract
We present a new way called Persona Analysis with Text Topic Modelling (PATTM), which tries to learn the role of personae according to the literal descriptions. It is similar to Latent Dirichlet Allocation (LDA) and Author Topic (AT) model, with the attribute of allowing all text to join in the topic modelling process, even when there is no persona in the text. We experiment on the “Libya Event” data set which contains more than 4,000 texts collected from the Internet. The PATTM gives lower perplexity than LDA and AT model on the data set.
Keywords
Gibbs sampling; machine learning; natual language process; topic model;
fLanguage
English
Publisher
iet
Conference_Titel
Automatic Control and Artificial Intelligence (ACAI 2012), International Conference on
Conference_Location
Xiamen
Electronic_ISBN
978-1-84919-537-9
Type
conf
DOI
10.1049/cp.2012.1280
Filename
6492887
Link To Document