The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Determining the sentiment of opinions
Open accessAbstract
Identifying sentiments (the affective parts of opinions) is a challenging problem.We present a system that, given a topic, automatically finds the people who hold opinions about that topic and the sentiment of each opinion.The system contains a module for determining word sentiment and another for combining sentiments within a sentence.We experiment with various models of classifying and combining sentiment at word and sentence levels, with promising results.
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
Toulmin, S. E. (2003) · doi:10.1017/cbo9780511840005 · Cited by 7,101
Cited by
Lexicon-based methods for sentiment analysis
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011) · Computational Linguistics · doi:10.1162/coli_a_00049 · Cited by 3,301
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700
Ghiassi, M., Skinner, J., & Zimbra, D. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.05.057 · Cited by 485
Survey on mining subjective data on the web
Tsytsarau, M., & Palpanas, T. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-011-0238-6 · Cited by 443
Are they different? Affect, feeling, emotion, sentiment, and opinion detection in text
Munezero, M., Montero, C. S., Sutinen, E., & Pajunen, J. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2317187 · Cited by 374
Metadata
- Authors
- Kim, S.-M., & Hovy, E.
- Venue
- Proceedings of the 20th international conference on Computational Linguistics - COLING '04
- Published
- 2004
- Pages
- 1367-es
- ISSN
- 1525-2477 (ISSN-L)
- OpenAlex
- W2112422413
- Cited by
- 1,482
- References
- 1
- FWCI
- 14.31
- 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: Determining the sentiment of opinions. https://app.cochat.ai/papers/10.3115/1220355.1220555
- The paper
- Kim, S.-M., & Hovy, E. (2004). Determining the sentiment of opinions. Proceedings of the 20th international conference on Computational Linguistics - COLING '04. https://doi.org/10.3115/1220355.1220555
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.
