Quality of Sentiment Analysis Tools: The Reasons of Inconsistency
Summary: Evaluation of six state-of-the-art sentiment tools on five datasets to test consistency for paraphrased texts. Proposes a data-quality heuristic for augmented data and inconsistency metrics; highlights intra- and inter-tool variability. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Wissam Mammar Kouadri (IMBA Consulting; University of Paris)
- 2. Mourad Ouziri (University of Paris)
- 3. Salima Benbernoun (University of Paris)
- 4. Karima Echihabi (Mohammed V University)
- 5. Themis Palpanas (French University Institute; University of Paris)
- 6. Iheb Ben Amor (IMBA Consulting)
BibTeX Citation
@article{kouadri_vldb21,
title = {{Quality of Sentiment Analysis Tools: The Reasons of Inconsistency}},
author = {Kouadri, Wissam Mammar and Ouziri, Mourad and Benbernoun, Salima and Echihabi, Karima and Palpanas, Themis and Amor, Iheb Ben},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {4},
pages = {668--681},
doi = {10.14778/3436905.3436924},
url = {https://doi.org/10.14778/3436905.3436924},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,599 | SA-Q: Observing, Evaluating, and Enhancing the Quality of the Results of Sentiment Analysis Tools | 2022 | VLDB | 5.093636e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 205 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00025235185 |
| 2,041 | Combining Quantitative and Logical Data Cleaning | 2016 | VLDB | 9.2692538e-05 |
| 2,096 | An Analysis of Structured Data on the Web | 2012 | VLDB | 9.1746663e-05 |
| 3,231 | Truth Inference in Crowdsourcing: Is the Problem Solved? | 2017 | VLDB | 7.6191767e-05 |
| 4,118 | Rafiki: Machine Learning as an Analytics Service System | 2019 | VLDB | 6.8908973e-05 |
| 5,367 | Tweets as Data: Demonstration of TweeQL and TwitInfo | 2011 | SIGMOD | 6.2448491e-05 |
| 7,251 | Efficient Sentiment Correlation for Large-scale Demographics | 2013 | SIGMOD | 5.6630343e-05 |
| 9,074 | Crowdsourcing Practice for Efficient Data Labeling: Aggregation, Incremental Relabeling, and Pricing | 2020 | SIGMOD | 5.3251649e-05 |
| 9,078 | Towards Personalized Maps: Mining User Preferences from Geo-textual Data | 2016 | VLDB | 5.3251649e-05 |
| 9,083 | Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media | 2014 | SIGMOD | 5.3251649e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 4,746 | On Detecting Cherry-picked Trendlines | 2020 | VLDB |
| 2 | 3,231 | Truth Inference in Crowdsourcing: Is the Problem Solved? | 2017 | VLDB |
| 3 | 6,544 | Properties of Inconsistency Measures for Databases | 2021 | SIGMOD |
| 4 | 8,067 | Mining Quality Phrases from Massive Text Corpora | 2015 | SIGMOD |
| 5 | 1,351 | Detecting Data Errors: Where are we and what needs to be done? | 2016 | VLDB |
| 6 | 533 | Improving Data Quality: Consistency and Accuracy | 2007 | VLDB |
| 7 | 9,083 | Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media | 2014 | SIGMOD |
| 8 | 12,282 | Aggregating Semantic Annotators | 2013 | VLDB |
| 9 | 7,251 | Efficient Sentiment Correlation for Large-scale Demographics | 2013 | SIGMOD |
| 10 | 11,599 | SA-Q: Observing, Evaluating, and Enhancing the Quality of the Results of Sentiment Analysis Tools | 2022 | VLDB |