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Efficient Sentiment Correlation for Large-scale Demographics

Summary: Scalable sentiment indexing and aggregation for demographic groups across time using online incremental structures across granularities. Efficient correlations via demographic pruning and top-k compression, with both synthetic and real-data experiments. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
ha67d3f4e3cd299eb
Venue
SIGMOD
Year
2013
Pagerank
5.5380047e-05
Overall Rank
7,388 | 50.33%
DOI
10.1145/2463676.2465317

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{tsytsarau_sigmod13,
        title = {{Efficient Sentiment Correlation for Large-scale Demographics}},
        author = {Tsytsarau, Mikalai and Amer-Yahia, Sihem and Palpanas, Themis},
        series = {{SIGMOD} '13},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2463676.2465317},
        url = {https://dl.acm.org/doi/10.1145/2463676.2465317},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
6,162 Guided Exploration of User Groups 2020 VLDB 5.8644841e-05
7,203 Mining Subjective Properties on the Web 2015 SIGMOD 5.5865623e-05
12,038 Quality of Sentiment Analysis Tools: The Reasons of Inconsistency 2021 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

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