The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Automatically assessing review helpfulness
Open accessAbstract
User-supplied reviews are widely and increasingly used to enhance e-commerce and other websites. Because reviews can be numerous and varying in quality, it is important to assess how helpful each review is. While review helpfulness is currently assessed manually, in this paper we consider the task of automatically assessing it. Experiments using SVM regression on a variety of features over Amazon.com product reviews show promising results, with rank correlations of up to 0.66. We found that the most useful features include the length of the review, its unigrams, and its product rating.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
The proof and measurement of association between two things
Spearman, C. (1904) · The American Journal of Psychology · doi:10.2307/1412159 · Cited by 8,039
Cited by
Finding Deceptive Opinion Spam by Any Stretch of the Imagination
Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011) · arXiv · doi:10.48550/arxiv.1107.4557 · Cited by 1,008
Spotting fake reviewer groups in consumer reviews
Mukherjee, A., Liu, B., & Glance, N. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187863 · Cited by 773
Hsu, C.-L., & Lin, J. C.-C. (2015) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2014.11.003 · Cited by 662
PeerStudio: Rapid Peer Feedback Emphasizes Revision and Improves Performance
Kulkarni, C. E., Bernstein, M. S., & Klemmer, S. R. (2015) · Proceedings of the Second (2015) ACM Conference on Learning @ Scale · doi:10.1145/2724660.2724670 · Cited by 192
Reputation Transferability in Online Labor Markets
Kokkodis, M., & Ipeirotis, P. G. (2016) · Management Science · doi:10.1287/mnsc.2015.2217 · Cited by 166
Metadata
- Authors
- Kim, S.-M., Pantel, P., Chklovski, T., & Pennacchiotti, M.
- Venue
- Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06
- Published
- 2006
- Pages
- 423
- OpenAlex
- W2087294982
- Cited by
- 518
- References
- 2
- FWCI
- 9.12
- Type
- conference paper
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Automatically assessing review helpfulness. https://app.cochat.ai/papers/10.3115/1610075.1610135
- The paper
- Kim, S.-M., Pantel, P., Chklovski, T., & Pennacchiotti, M. (2006). Automatically assessing review helpfulness. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06. https://doi.org/10.3115/1610075.1610135
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
