Name
Abstract | ||
|---|---|---|
Community Question Answering (CQA) is becoming a popular Web 2.0 application. By analyzing evolutionary topic patterns from CQA applications, one can gain insights into user interests and user responses to external events. This paper proposes a novel evolutionary topic pattern mining approach. This approach consists of three components: 1) extraction of the topics being discussed through a temporal analysis; 2) discovery of topic evolutions and construction of evolutionary graphs of extracted topics; and 3) life cycle modeling of the extracted topics. We show empirically the effectiveness of our approach using two real-world data sets. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TSMCA.2011.2157131 | IEEE Transactions on Systems, Man, and Cybernetics, Part A |
Keywords | Field | DocType |
user response,evolutionary topic patterns,community question answering (cqa),external event,evolutionary graph,evolutionary topic pattern,community question answering,cqa application,topic evolution,mining approach,user interest,life cycle,novel evolutionary topic pattern,community question answering systems,mining evolutionary topic patterns,probabilistic logic,energy states,economics,question answering,question answering system | Data science,Graph,Data set,Question answering,Information retrieval,Computer science,Probabilistic logic | Journal |
Volume | Issue | ISSN |
41 | 5 | 1083-4427 |
Citations | PageRank | References |
7 | 0.44 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Z. Zhang | 1 | 7 | 0.44 |
| Q. Li | 2 | 7 | 0.44 |
| Daniel Zeng | 3 | 2539 | 286.59 |