Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media

Summary: Unsupervised tri-clustering on a tripartite graph jointly clusters tweets, users, and features to mutually improve tweet- and user-level sentiment. An online algorithm updates clusters with streaming data, enabling dynamic sentiment tracking and storage-efficient computation, demonstrated on ballot Twitter data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 4812
Venue: SIGMOD
Year: 2014
Pagerank: 4.427232e-05
Overall Rank: 8,928 | 37.89%
DOI: 10.1145/2588555.2593682

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,538	Quality of Sentiment Analysis Tools: The Reasons of Inconsistency	2021	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
2,807	A Model-based Approach to Attributed Graph Clustering	2012	SIGMOD	8.0905959e-05
3,601	Large-Scale Machine Learning at Twitter	2012	SIGMOD	6.9315087e-05
5,859	LCI: A Social Channel Analysis Platform for Live Customer Intelligence	2011	SIGMOD	5.2994829e-05

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