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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
4874
Venue
SIGMOD
Year
2014
Pagerank
5.3251649e-05
Overall Rank
9,083 | 37.69%
DOI
10.1145/2588555.2593682

Incoming Non-self Citations Over Time

Authors

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
Previous Page 1 / 1 Next

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