Paper Info
Title
Detecting Topics from Twitter Posts During TV Program Viewing
Abstract
This research proposes a method to detect the contents of Twitter posts by analyzing the contents of tweets posted by viewers watching a specific TV program whenever the number of posts increase dramatically and then to summarize that content. First the proposed method creates concepts from clusters based on the co-occurrence of words. Then posts during tweet bursts and posts that match the contents of the TV program dialog are taken to be tweets of interest, and a minimal number of clusters that cover as much as possible those tweets are extracted using a knapsack-constrained maximum covering problem. The extracted clusters are thought to express topics obtained from the tweets of interest, and thus post contents related to specific objectives can be abstracted from a huge amount of tweets. A computational experiment shows the effectiveness of the proposed method with reference to a TV animation program "Space Brothers."
Year
DOI
Venue
2013
10.1109/ICDMW.2013.48
Data Mining Workshops
Keywords
Field
DocType
detecting topics,specific objective,twitter post,tv program dialog,huge amount,tv animation program,computational experiment,space brothers,specific tv program,tv program viewing,minimal number,edit distance,information analysis
Dialog box,Edit distance,Data mining,Data modeling,World Wide Web,Computer science,Pattern clustering,Animation,Hidden Markov model,Micro cluster
Conference
ISSN
ISBN
Citations 
2375-9232
978-1-4799-3143-9
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Takanobu Nakahara1284.46
Yukinobu Hamuro2437.76