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 (University of Southern California)
- 2. Aram Galstyan (University of Southern California)
- 3. James Cheng (Chinese University of Hong Kong)
- 4. Kristina Lerman (University of Southern California)
BibTeX Citation
@inproceedings{zhu_sigmod14,
title = {{Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media}},
author = {Zhu, Linhong and Galstyan, Aram and Cheng, James and Lerman, Kristina},
series = {{SIGMOD} '14},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2588555.2593682},
url = {https://dl.acm.org/doi/10.1145/2588555.2593682},
year = {2014}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,735 | Quality of Sentiment Analysis Tools: The Reasons of Inconsistency | 2021 | VLDB | 5.093636e-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 |
|---|---|---|---|---|
| 2,644 | A Model-based Approach to Attributed Graph Clustering | 2012 | SIGMOD | 8.3043854e-05 |
| 3,714 | Large-Scale Machine Learning at Twitter | 2012 | SIGMOD | 7.1764857e-05 |
| 6,834 | LCI: A Social Channel Analysis Platform for Live Customer Intelligence | 2011 | SIGMOD | 5.7592816e-05 |
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