TY - GEN
T1 - Micro-blog post topic drift detection based on LDA model
AU - Liu, Quanchao
AU - Huang, Heyan
AU - Feng, Chong
PY - 2013
Y1 - 2013
N2 - Micro-blog posts imply a large number of topics, which contain a lot of useful information as well as a lot of junk information making the micro-blog post topic a characteristic of high drift. The changes of micro-blog post topic over time and noises introduced with the increase of the number of micro-blog posts are two main aspects of micro-blog post topic drift. We propose a method of topic drift detection based on LDA model, using Gibbs sampling algorithm to obtain the probability distribution of micro-blog post words based on words correlation, identifying the topic boundary in dynamic constant method, extracting topic words by computing lexical information entropy in the topic field, and detecting the topic drift by topic words sequence alignment based on discrete-time model. According to the experiment on topic drift detection based on LDA model, we find our method very effective in micro-blog post topic drift detection.
AB - Micro-blog posts imply a large number of topics, which contain a lot of useful information as well as a lot of junk information making the micro-blog post topic a characteristic of high drift. The changes of micro-blog post topic over time and noises introduced with the increase of the number of micro-blog posts are two main aspects of micro-blog post topic drift. We propose a method of topic drift detection based on LDA model, using Gibbs sampling algorithm to obtain the probability distribution of micro-blog post words based on words correlation, identifying the topic boundary in dynamic constant method, extracting topic words by computing lexical information entropy in the topic field, and detecting the topic drift by topic words sequence alignment based on discrete-time model. According to the experiment on topic drift detection based on LDA model, we find our method very effective in micro-blog post topic drift detection.
KW - Evolutionary analysis
KW - LDA model
KW - Micro-blog post
KW - Topic drift
KW - Topic drift detection
UR - http://www.scopus.com/inward/record.url?scp=84894120920&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-04048-6_10
DO - 10.1007/978-3-319-04048-6_10
M3 - Conference contribution
AN - SCOPUS:84894120920
SN - 9783319040479
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 106
EP - 118
BT - Behavior and Social Computing - Int. Workshop on Behavior and Social Informatics, BSI 2013 and Int. Workshop on Behavior and Social Informatics and Computing, BSIC 2013, Revised Selected Papers
PB - Springer Verlag
T2 - 2013 International Workshop on Behavior and Social Informatics and Computing, BSIC 2013
Y2 - 3 August 2013 through 9 August 2013
ER -