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)
Incoming Non-self Citations Over Time
Authors
- 1. Linhong Zhu
- 2. Aram Galstyan
- 3. James Cheng
- 4. Kristina Lerman
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,542 | Quality of Sentiment Analysis Tools: The Reasons of Inconsistency | 2021 | VLDB | 4.1905499e-05 |
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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 |
|---|---|---|---|---|
| 2,810 | A Model-based Approach to Attributed Graph Clustering | 2012 | SIGMOD | 8.085151e-05 |
| 3,606 | Large-Scale Machine Learning at Twitter | 2012 | SIGMOD | 6.9246371e-05 |
| 5,866 | LCI: A Social Channel Analysis Platform for Live Customer Intelligence | 2011 | SIGMOD | 5.2943942e-05 |
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