The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Extracting opinions, opinion holders, and topics expressed in online news media text
Open accessAbstract
This paper presents a method for identifying an opinion with its holder and topic, given a sentence from online news media texts. We introduce an approach of exploiting the semantic structure of a sentence, anchored to an opinion bearing verb or adjective. This method uses semantic role labeling as an intermediate step to label an opinion holder and topic using data from FrameNet. We decompose our task into three phases: identifying an opinion-bearing word, labeling semantic roles related to the word in the sentence, and then finding the holder and the topic of the opinion word among the labeled semantic roles. For a broader coverage, we also employ a clustering technique to predict the most probable frame for a word which is not defined in FrameNet. Our experimental results show that our system performs significantly better than the baseline.
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
Harris, Z. S. (1954) · WORD · doi:10.1080/00437956.1954.11659520 · Cited by 4,807
Cited by
Affective News: The Automated Coding of Sentiment in Political Texts
Young, L., & Soroka, S. (2012) · Political Communication · doi:10.1080/10584609.2012.671234 · Cited by 609
Towards detecting influenza epidemics by analyzing Twitter messages
Culotta, A. (2010) · Proceedings of the First Workshop on Social Media Analytics · doi:10.1145/1964858.1964874 · Cited by 608
A sentiment analysis model for hotel reviews based on supervised learning
Shi, H.-X., & Li, X.-J. (2011) · 2011 International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2011.6016866 · Cited by 105
Identifying Controversial Issues and Their Sub-topics in News Articles
Choi, Y., Jung, Y., & Myaeng, S.-H. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-13601-6_16 · Cited by 59
Metadata
- Authors
- Kim, S.-M., & Hovy, E.
- Venue
- Proceedings of the Workshop on Sentiment and Subjectivity in Text - SST '06
- Published
- 2006
- Pages
- 1-8
- OpenAlex
- W2011450768
- Cited by
- 460
- References
- 1
- FWCI
- 13.93
- Type
- conference paper
- Language
- en
- Open access
- gold
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: Extracting opinions, opinion holders, and topics expressed in online news media text. https://app.cochat.ai/papers/10.3115/1654641.1654642
- The paper
- Kim, S.-M., & Hovy, E. (2006). Extracting opinions, opinion holders, and topics expressed in online news media text. Proceedings of the Workshop on Sentiment and Subjectivity in Text - SST '06. https://doi.org/10.3115/1654641.1654642
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.
