---
title: "OpinionFinder: A System for Subjectivity Analysis (2005)"
description: "OpinionFinder is a demonstration system that automatically detects subjective language in English… 11 references, cited by 78 papers here."
authors: ["Theresa Wilson", "Paul Hoffmann", "Swapna Somasundaran", "Jason Kessler", "Janyce Wiebe", "Yejin Choi", "Claire Cardie", "Ellen Riloff", "Siddharth Patwardhan"]
year: 2005
venue: "Proceedings of HLT/EMNLP 2005 Interactive Demonstrations"
source: "https://aclanthology.org/H05-2018/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wilson-2005-opinionfinder-system-subjectivity-analysis/"
---
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# OpinionFinder: A System for Subjectivity Analysis

**Authors:** [Theresa Wilson](https://md.research.elamur.ai/free/en/authors/theresa-wilson/), [Paul Hoffmann](https://md.research.elamur.ai/free/en/authors/paul-hoffmann/), [Swapna Somasundaran](https://md.research.elamur.ai/free/en/authors/swapna-somasundaran/), Jason Kessler, [Janyce Wiebe](https://md.research.elamur.ai/free/en/authors/janyce-wiebe/), [Yejin Choi](https://md.research.elamur.ai/free/en/authors/yejin-choi/), [Claire Cardie](https://md.research.elamur.ai/free/en/authors/claire-cardie/), [Ellen Riloff](https://md.research.elamur.ai/free/en/authors/ellen-riloff/) and [Siddharth Patwardhan](https://md.research.elamur.ai/free/en/authors/siddharth-patwardhan/) · **Year:** 2005 · **Venue:** [Proceedings of HLT/EMNLP 2005 Interactive Demonstrations](https://md.research.elamur.ai/free/en/venues/hlt/) ([HLT 2005](https://md.research.elamur.ai/free/en/venues/hlt/2005/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/H05-2018/) · [PDF](https://aclanthology.org/H05-2018.pdf) · [OpenAlex](https://openalex.org/W7099880000)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [78](https://md.research.elamur.ai/free/en/papers/wilson-2005-opinionfinder-system-subjectivity-analysis/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
OpinionFinder is a demonstration system that automatically detects subjective language in English text, meaning opinions, emotions, speculations and other private states. Its purpose is to act as a support component for downstream NLP tasks. Examples include opinion-oriented question answering, summarizing different viewpoints, mining product reviews, and filtering opinionated sentences out of information extraction. The system is a single pipeline. A general preprocessing stage runs first: Sundance partial parsing, OpenNLP tokenization, sentence splitting and POS tagging, stemming, optional dependency parsing, and lexicon clue matching. A subjectivity stage follows, built from four components:

* sentence-level subjective/objective classification,
* detection of speech events and direct subjective expressions,
* identification of opinion sources (holders),
* marking of words inside positive or negative sentiment phrases.

Output is delivered as SGML/XML annotations layered over the original documents. The system works in a batch mode or an interactive mode that queries online news. The contribution is an integrated, reusable tool rather than a new benchmark result. The paper itself reports no evaluation numbers and points readers to the component papers.

## Key points
- The subjective sentence classifier is Naive Bayes. Its training sentences come from unannotated text that was automatically labeled by two high-precision rule-based classifiers.
- Opinion source identification combines a Conditional Random Field tagger with extraction pattern learning. It is trained on the MPQA Opinion Corpus.
- Sentiment expression detection uses two BoosTexter classifiers trained on the MPQA Corpus. One finds sentiment expressions, and the other separates positive from negative ones.
- Preprocessing tools:
  - Sundance for semantic classes, named entities and subjective extraction patterns.
  - OpenNLP 1.1.0 for tokenization, sentence splitting and POS tagging.
  - The Abney stemmer.
  - Collins constituency parses converted to dependency trees (Xia and Palmer).
- Dependency parsing is slow, so it runs only in batch mode. The source identifier depends on it, so source identification is also batch-only.

## Interesting ideas
- **Bootstrapping training data with precise rules.** High-precision rule-based classifiers can label raw text to create training data for a statistical classifier. This could help bootstrap subjectivity or other classifiers for low-resource languages such as Ukrainian, where annotated corpora are scarce.
- **Annotations over the original text.** Results are returned as markup on the source documents rather than in a separate format. This keeps the analysis traceable and easy for other tools to consume.
- **Speed-dependent feature tiers.** Expensive components (full parsing, and anything that depends on it) are enabled only in offline batch runs, while the interactive mode uses a lighter pipeline. This is a practical way to trade accuracy for latency.
- **Sequence labeling plus pattern learning.** Opinion holders are found by combining a CRF tagger with learned extraction patterns. Mixing statistical tagging with pattern knowledge could transfer to other span-finding tasks, such as identifying who made a statement in news or trade reports.
- **Subjectivity filtering as a preprocessing step.** Removing opinionated sentences can also benefit fact-oriented pipelines like information extraction, which makes subjectivity detection useful beyond sentiment applications.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Theresa Wilson, Paul Hoffmann, Swapna Somasundaran, Jason Kessler, Janyce Wiebe, Yejin Choi, Claire Cardie, Ellen Riloff and Siddharth Patwardhan. 2005. OpinionFinder: A System for Subjectivity Analysis. Proceedings of HLT/EMNLP 2005 Interactive Demonstrations.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wilson2005opinionfinder,
  title = {{OpinionFinder: A System for Subjectivity Analysis}},
  author = {Theresa Wilson, Paul Hoffmann, Swapna Somasundaran, Jason Kessler, Janyce Wiebe, Yejin Choi, Claire Cardie, Ellen Riloff and Siddharth Patwardhan},
  booktitle = {Proceedings of HLT/EMNLP 2005 Interactive Demonstrations},
  year = {2005},
  url = {https://aclanthology.org/H05-2018/}
}
```

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## References

11 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Y. Choi, C. Cardie, E. Riloff, and S. Patwardhan. 2005. [Identifying sources of opinions with conditional random fields and extraction patterns](https://md.research.elamur.ai/free/en/papers/choi-2005-identifying-sources-opinions-conditional-random/). In HLT/EMNLP 2005.
2. M. Collins. 1997. [Three generative, lexicalised models for statistical parsing](https://md.research.elamur.ai/free/en/papers/collins-1997-three-generative-lexicalised-models-statistical/). In ACL-1997.
3. J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML-2001. [find](https://scholar.google.com/scholar?q=J%2E+Lafferty%2C+A%2E+McCallum%2C+and+F%2E+Pereira%2E+2001%2E+Conditional+random+fields%3A+Probabilistic+models+for+segmenting+and+labeling+sequence+data%2E+In+ICML%2D2001%2E)
4. E. Riloff and W. Phillips. 2004. An Introduction to the Sundance and AutoSlog Systems. Technical Report UUCS-04-015, School of Computing, University of Utah. [find](https://scholar.google.com/scholar?q=E%2E+Riloff+and+W%2E+Phillips%2E+2004%2E+An+Introduction+to+the+Sundance+and+AutoSlog+Systems%2E+Technical+Report+UUCS%2D04%2D015%2C+School+of+Computing%2C+University+of+Utah%2E)
5. E. Riloff and J. Wiebe. 2003. [Learning extraction patterns for subjective expressions](https://md.research.elamur.ai/free/en/papers/riloff-2003-learning-extraction-patterns-subjective-expressions/). In EMNLP-2003.
6. E. Riloff, J. Wiebe, and W. Phillips. 2005. Exploiting subjectivity classification to improve information extraction. In AAAI-2005. [find](https://scholar.google.com/scholar?q=E%2E+Riloff%2C+J%2E+Wiebe%2C+and+W%2E+Phillips%2E+2005%2E+Exploiting+subjectivity+classification+to+improve+information+extraction%2E+In+AAAI%2D2005%2E)
7. E. Riloff. 1996. An Empirical Study of Automated Dictionary Construction for Information Extraction in Three Domains. Artificial Intelligence, 85:101–134. [find](https://scholar.google.com/scholar?q=E%2E+Riloff%2E+1996%2E+An+Empirical+Study+of+Automated+Dictionary+Construction+for+Information+Extraction+in+Three+Domains%2E+Artificial+Intelligence%2C+85%3A101%E2%80%93134%2E)
8. R. E. Schapire and Y. Singer. 2000. BoosTexter: A boosting-based system for text categorization. Machine Learning, 39(2/3):135–168. [find](https://scholar.google.com/scholar?q=R%2E+E%2E+Schapire+and+Y%2E+Singer%2E+2000%2E+BoosTexter%3A+A+boosting%2Dbased+system+for+text+categorization%2E+Machine+Learning%2C+39%282%2F3%29%3A135%E2%80%93168%2E)
9. J. Wiebe and E. Riloff. 2005. Creating subjective and objective sentence classifiers from unannotated texts. In CICLing-2005. [find](https://scholar.google.com/scholar?q=J%2E+Wiebe+and+E%2E+Riloff%2E+2005%2E+Creating+subjective+and+objective+sentence+classifiers+from+unannotated+texts%2E+In+CICLing%2D2005%2E)
10. T. Wilson, J. Wiebe, and P. Hoffmann. 2005. [Recognizing contextual polarity in phrase-level sentiment analysis](https://md.research.elamur.ai/free/en/papers/wilson-2005-recognizing-contextual-polarity-phrase-level/). In HLT/EMNLP 2005.
11. F. Xia and M. Palmer. 2001. Converting dependency structures to phrase structures. In HLT-2001. [find](https://scholar.google.com/scholar?q=F%2E+Xia+and+M%2E+Palmer%2E+2001%2E+Converting+dependency+structures+to+phrase+structures%2E+In+HLT%2D2001%2E)

*Updated 24 October 2026*