---
title: "Opinion Word Expansion and Target Extraction — Qiu 2011"
description: "Opinion mining relies on two building blocks. The first is a sentiment lexicon that fits the domain. The second is knowing what each opinion refers to…"
authors: ["Guang Qiu", "Bing Liu", "Jiajun Bu", "Chun Chen"]
year: 2011
venue: "Computational Linguistics"
source: "https://aclanthology.org/J11-1002/"
doi: "10.1162/coli_a_00034"
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/qiu-2011-opinion-word-expansion-target-extraction/"
---
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# Opinion Word Expansion and Target Extraction through Double Propagation

**Authors:** Guang Qiu, [Bing Liu](https://md.research.elamur.ai/free/en/authors/bing-liu/), Jiajun Bu, Chun Chen · **Year:** 2011 · **Venue:** 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/J11-1002/) · [PDF](https://aclanthology.org/J11-1002.pdf) · [DOI 10.1162/coli\_a\_00034](https://doi.org/10.1162/coli_a_00034) · [OpenAlex](https://openalex.org/W2159457224)

**Citations:** 1074 citations (openalex-snapshot · 2026-10-14) · [122](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Opinion mining relies on two building blocks. The first is a sentiment lexicon that fits the domain. The second is knowing what each opinion refers to, called the target, such as a product feature like battery life. General-purpose lexicons miss domain-specific polarity. Earlier target extractors also depended on Web queries, a known product class, or supervised training. Qiu et al. solve both tasks together with a semi-supervised bootstrapping method they call double propagation. It starts from a small seed set of opinion words. A dependency parser then finds three kinds of syntactic links: opinion word to target, opinion word to opinion word, and target to target. Using these links, the method alternates between collecting new opinion words and new noun targets from the items already known. Extra steps assign polarity to newly found opinion words and remove noisy targets. On a standard product review collection of five data sets, the method outperformed state-of-the-art baselines on both tasks. Its main practical value is that it needs only the seeds and in-domain reviews, with no external resources.

## Key points
- The relations are defined with dependency grammar and labeled OT-Rel (opinion–target), OO-Rel (opinion–opinion) and TT-Rel (target–target). Extracted targets are limited to nouns.
- Target extraction, averaged over data sets D1–D5, scored precision 0.88, recall 0.83 and F-score 0.86 (Prop-dep). Baseline F-scores were 0.82 for Popescu, 0.76 for Hu, 0.54 for PLSA, 0.54 for CRF-D and 0.43 for CRF. Paired t-tests found the gains significant at the 95% level.
- An ablation without propagation (noProp-dep) reached the highest precision (0.94) but recall fell to 0.66 (F 0.77). The rules themselves are accurate, and propagation is what drives coverage, at the cost of some added noise.
- Among the baselines, Popescu requires Web search and a known product class. PLSA tends to surface coarse topics instead of fine-grained targets. The Hu and Popescu scores were taken from their original papers.
- Planned future work covers pruning methods to raise opinion-word precision and learning syntactic relations automatically through pattern mining.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

122 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 114 not yet.

- [Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions](https://md.research.elamur.ai/free/en/papers/cai-2021-aspect-category-opinion-sentiment-quadruple/)
- [An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/he-2019-interactive-multi-task-learning-network/)
- [Sarcasm Target Identification: Dataset and An Introductory Approach](https://md.research.elamur.ai/free/en/papers/joshi-2018-sarcasm-target-identification-dataset-introductory/)
- [Open Domain Targeted Sentiment](https://md.research.elamur.ai/free/en/papers/mitchell-2013-open-domain-targeted-sentiment/)
- [Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/wang-2016-recursive-neural-conditional-random-fields/)
- [Relational Graph Attention Network for Aspect-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/wang-2020-relational-graph-attention-network-aspect/)
- [Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction](https://md.research.elamur.ai/free/en/papers/xu-2018-double-embeddings-cnn-based-sequence/)
- [Position-Aware Tagging for Aspect Sentiment Triplet Extraction](https://md.research.elamur.ai/free/en/papers/xu-2020-position-aware-tagging-aspect-sentiment/)

## Cite

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

```bibtex
@article{qiu2011opinion,
  title = {{Opinion Word Expansion and Target Extraction through Double Propagation}},
  author = {Qiu, Guang and Liu, Bing and Bu, Jiajun and Chen, Chun},
  journal = {Computational Linguistics},
  year = {2011},
  doi = {10.1162/coli\_a\_00034},
  url = {https://doi.org/10.1162/coli_a_00034}
}
```

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