SA-Q: Observing, Evaluating, and Enhancing the Quality of the Results of Sentiment Analysis Tools
Summary: SA-Q is a demonstrator for measuring intra- and inter-tool inconsistency in sentiment-analysis outputs. It applies inconsistency-resolution methods and dataset-specific tool recommendation, drawing on truth inference to improve robustness and scalability. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Wissam Maamar-Kouadri (Université Paris Cité)
- 2. Salima Benbernou (Université Paris Cité)
- 3. Mourad Ouziri (Université Paris Cité)
- 4. Themis Palpanas (French University Institute; Université Paris Cité)
- 5. Iheb Ben Amor (IMBA Consulting)
BibTeX Citation
@article{maamarkouadri_vldb22,
title = {{SA-Q: Observing, Evaluating, and Enhancing the Quality of the Results of Sentiment Analysis Tools}},
author = {Maamar-Kouadri, Wissam and Benbernou, Salima and Ouziri, Mourad and Palpanas, Themis and Amor, Iheb Ben},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {12},
pages = {3658--3661},
doi = {10.14778/3554821.3554868},
url = {https://doi.org/10.14778/3554821.3554868},
year = {2022}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,171 | Truth Inference in Crowdsourcing: Is the Problem Solved? | 2017 | VLDB | 7.5691557e-05 |
| 12,038 | Quality of Sentiment Analysis Tools: The Reasons of Inconsistency | 2021 | VLDB | 4.9793485e-05 |
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