Difference between revisions of "Real Time Twitter Filtering Framework"
From Knoesis wiki
(→References) |
(→References) |
||
| Line 10: | Line 10: | ||
=Tasks= | =Tasks= | ||
=References= | =References= | ||
| + | ==Classification== | ||
| + | ==Clustering== | ||
| + | ==Active Learning or Semi supervised learning on Twitter== | ||
#[http://dl.acm.org/citation.cfm?id=1964870 Empirical Study of Topic Modeling on Twitter] | #[http://dl.acm.org/citation.cfm?id=1964870 Empirical Study of Topic Modeling on Twitter] | ||
#[http://link.springer.com/chapter/10.1007/978-3-642-29038-1_29 Searching for Quality Microblog Posts: Filtering and Ranking Based on Content Analysis and Implicit Links] | #[http://link.springer.com/chapter/10.1007/978-3-642-29038-1_29 Searching for Quality Microblog Posts: Filtering and Ranking Based on Content Analysis and Implicit Links] | ||
| Line 15: | Line 18: | ||
#[http://www.websci11.org/fileadmin/websci/papers/147_paper.pdf Small worlds with a difference: New gatekeepers and the filtering of political information on twitter] | #[http://www.websci11.org/fileadmin/websci/papers/147_paper.pdf Small worlds with a difference: New gatekeepers and the filtering of political information on twitter] | ||
#[http://knoesis.org/library/download/Chen2014ACL.pdf Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy] | #[http://knoesis.org/library/download/Chen2014ACL.pdf Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy] | ||
| + | #[http://www.aclweb.org/anthology/C12-1035 A Semi-Supervised Bayesian Network Model for Microblog Topic Classification] | ||
=People= | =People= | ||
Revision as of 04:25, 22 December 2014
Contents
Introduction
Twitter, a popular microblogging platform, generates approximately 500 Million tweets everyday. These tweets are filtered by diverse domains to analyze and gain insights into the opinion of online users on corresponding topics. For instance, brands monitor tweets to track their products' success and issues, journalists follow twitter to gain insights on real-time news and developments on certain issues.
Architecture and Approach
Tweet Topic Classification
Clustering of Tweets
Top K Ranking of Tweets for Clusters
Evaluation
Tasks
References
Classification
Clustering
Active Learning or Semi supervised learning on Twitter
- Empirical Study of Topic Modeling on Twitter
- Searching for Quality Microblog Posts: Filtering and Ranking Based on Content Analysis and Implicit Links
- Semantics + filtering + search = twitcident. exploring information in social web streams
- Small worlds with a difference: New gatekeepers and the filtering of political information on twitter
- Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy
- A Semi-Supervised Bayesian Network Model for Microblog Topic Classification
People
- Pavan Kapanipathi
- Alan Smith
- Adarsh Alex
