---
title: "Co-Training for Cross-Lingual Sentiment — Wan 2009"
description: "The paper addresses polarity classification of Chinese product reviews when labeled Chinese training data is unavailable but annotated English reviews…"
authors: ["Xiaojun Wan"]
year: 2009
venue: "Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP"
source: "https://aclanthology.org/P09-1027/"
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/wan-2009-co-training-cross-lingual-sentiment/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wan-2009-co-training-cross-lingual-sentiment/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2009](https://md.research.elamur.ai/free/en/papers/2009/) / Co-Training for Cross-Lingual Sentiment Classification

# Co-Training for Cross-Lingual Sentiment Classification

**Authors:** [Xiaojun Wan](https://md.research.elamur.ai/free/en/authors/xiaojun-wan/) · **Year:** 2009 · **Venue:** Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP · **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/P09-1027/) · [PDF](https://aclanthology.org/P09-1027.pdf) · [OpenAlex](https://openalex.org/W2167660864)

**Citations:** 487 citations (openalex-snapshot · 2026-10-14) · [107](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses polarity classification of Chinese product reviews when labeled Chinese training data is unavailable but annotated English reviews exist. Wan uses Google Translate in both directions. English training reviews are rendered into Chinese, and unlabeled and test Chinese reviews are rendered into English. Each review therefore has two versions. The English-side features and the Chinese-side features serve as two separate views, which fits the co-training framework of Blum and Mitchell. Two SVM classifiers, one per view, are bootstrapped on unlabeled Chinese reviews (about 1000, kept separate from the test data). In each round, each classifier adds its most confident positive and negative examples to the shared labeled pool. The two final classifiers are merged by averaging their predictions. The method was compared with eight baselines: inductive SVM and transductive SVM, each using Chinese-only, English-only, or both views. Co-training reached 0.813 accuracy, compared with 0.790 for the strongest baseline. This shows that machine translation combined with unlabeled target-language text can partly compensate for missing sentiment corpora in a new language.

## Key points
- Setup: labeled English reviews (a sentiment dataset hosted by M. Dredze at UPenn) plus unlabeled Chinese reviews (source: it168.com). Translation in both directions uses Google Translate, and SVM is the base learner.
- Baselines are SVM and TSVM in four variants each: CN, EN, ENCN1 (a single model on both feature sets), and ENCN2 (the averaged outputs of the EN and CN models).
- Total accuracy: CoTrain (I=40, p=n=5) 0.813, TSVM(ENCN2) 0.790, SVM(ENCN2) 0.775, SVM(CN) 0.771, SVM(EN) 0.738. CoTrain beats every baseline on all reported precision, recall, and F-measure values.
- Inside co-training, the separate English and Chinese classifiers score 0.775 and 0.790. Each is above its TSVM counterpart, and the Chinese one alone surpasses the best baseline for iteration counts of roughly 40–70.
- Accuracy rises after about 20 iterations and later falls slightly as noisy examples enter the pool. It then plateaus once the unlabeled pool is exhausted. Imbalanced growth, such as (1,5) or (5,1), makes accuracy decline steadily.

## Interesting ideas
- **Translation as view creation:** translating everything both ways turns a single-language problem into a two-view one. This makes co-training usable even without natural multi-view data, and it could transfer to Ukrainian NLP tasks that rely on English-labeled corpora.
- **Keep the views separate, then combine:** training per-language classifiers and averaging their scores worked better than merging all features into one model (ENCN2 > ENCN1). This is a cheap trick worth trying whenever two noisy feature sources exist.
- **Balanced self-labeling:** adding equal numbers of confident positives and negatives each round was essential, because skewed additions quickly degraded the model. Any bootstrapping or pseudo-labeling pipeline should respect class balance in the same way.
- **Blind test set during bootstrapping:** the Chinese reviews used for evaluation were excluded from the unlabeled pool. This protocol avoids optimistic transductive leakage and is worth copying in semi-supervised evaluations.
- **Report the full iteration curve:** plotting accuracy against the number of iterations exposes both the gain and the later drift caused by noisy examples. A curve gives a more honest picture than a single tuned operating point.

*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

107 papers of our library cite it (of the 140884 whose bibliographies we read); 2 of them have a page here, 105 not yet.

- [Lexicon-Based Methods for Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/taboada-2011-lexicon-based-methods-sentiment-analysis/)
- [End-to-End Slot Alignment and Recognition for Cross-Lingual NLU](https://md.research.elamur.ai/free/en/papers/xu-2020-end-end-slot-alignment-recognition/)

## Cite

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

```bibtex
@inproceedings{wan2009co,
  title = {{Co-Training for Cross-Lingual Sentiment Classification}},
  author = {Wan, Xiaojun},
  booktitle = {Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP},
  year = {2009},
  doi = {10.3115/1687878.1687913},
  url = {https://doi.org/10.3115/1687878.1687913}
}
```

- [← previous CATiB: The Columbia Arabic Treebank](https://md.research.elamur.ai/free/en/papers/aclbulk-2009-habash-catib-columbia-arabic-treebank/)
- [next → Comparison of Classification and Ranking Approaches to Pronominal Anaphora Resolution in Czech](https://md.research.elamur.ai/free/en/papers/y-2009-comparison-classification-ranking-approaches-pronominal/)
- [↑ back to papers of 2009](https://md.research.elamur.ai/free/en/papers/2009/)

## References

43 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. A. Andreevskaia and S. Bergler. 2008. When specialists and generalists work together: overcoming domain dependence in sentiment tagging. In Proceedings of ACL-08: HLT. [find](https://scholar.google.com/scholar?q=A%2E+Andreevskaia+and+S%2E+Bergler%2E+2008%2E+When+specialists+and+generalists+work+together%3A+overcoming+domain+dependence+in+sentiment+tagging%2E+In+Proceedings+of+ACL%2D08%3A+HLT%2E)
2. C. Banea, R. Mihalcea, J. Wiebe and S. Hassan. 2008. Multilingual subjectivity analysis using machine translation. In Proceedings of EMNLP-2008. [find](https://scholar.google.com/scholar?q=C%2E+Banea%2C+R%2E+Mihalcea%2C+J%2E+Wiebe+and+S%2E+Hassan%2E+2008%2E+Multilingual+subjectivity+analysis+using+machine+translation%2E+In+Proceedings+of+EMNLP%2D2008%2E)
3. N. Bel, C. H. A. Koster, and M. Villegas. 2003. Cross-lingual text categorization. In Proceedings of ECDL-03. [find](https://scholar.google.com/scholar?q=N%2E+Bel%2C+C%2E+H%2E+A%2E+Koster%2C+and+M%2E+Villegas%2E+2003%2E+Cross%2Dlingual+text+categorization%2E+In+Proceedings+of+ECDL%2D03%2E)
4. J. Blitzer, M. Dredze and F. Pereira. 2007. Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In Proceedings of ACL-07. [find](https://scholar.google.com/scholar?q=J%2E+Blitzer%2C+M%2E+Dredze+and+F%2E+Pereira%2E+2007%2E+Biographies%2C+bollywood%2C+boom%2Dboxes+and+blenders%3A+domain+adaptation+for+sentiment+classification%2E+In+Proceedings+of+ACL%2D07%2E) [our card](https://md.research.elamur.ai/free/en/papers/blitzer-2007-biographies-bollywood-boom-boxes-blenders/)
5. A. Blum and T. Mitchell. 1998. Combining labeled and unlabeled data with cotraining. In Proceedings of COLT-98. [find](https://scholar.google.com/scholar?q=A%2E+Blum+and+T%2E+Mitchell%2E+1998%2E+Combining+labeled+and+unlabeled+data+with+cotraining%2E+In+Proceedings+of+COLT%2D98%2E)
6. S. Brody, R. Navigli and M. Lapata. 2006. Ensemble methods for unsupervised WSD. In Proceedings of COLING-ACL-2006. [find](https://scholar.google.com/scholar?q=S%2E+Brody%2C+R%2E+Navigli+and+M%2E+Lapata%2E+2006%2E+Ensemble+methods+for+unsupervised+WSD%2E+In+Proceedings+of+COLING%2DACL%2D2006%2E)
7. S. Clark, J. R. Curran, and M. Osborne. 2003. Bootstrapping POS taggers using unlabelled data. In Proceedings of CoNLL-2003. [find](https://scholar.google.com/scholar?q=S%2E+Clark%2C+J%2E+R%2E+Curran%2C+and+M%2E+Osborne%2E+2003%2E+Bootstrapping+POS+taggers+using+unlabelled+data%2E+In+Proceedings+of+CoNLL%2D2003%2E)
8. W. Dai, G.-R. Xue, Q. Yang, Y. Yu. 2007a. Transferring Naïve Bayes Classifiers for text classification. In Proceedings of AAAI-07. [find](https://scholar.google.com/scholar?q=W%2E+Dai%2C+G%2E%2DR%2E+Xue%2C+Q%2E+Yang%2C+Y%2E+Yu%2E+2007a%2E+Transferring+Na%C3%AFve+Bayes+Classifiers+for+text+classification%2E+In+Proceedings+of+AAAI%2D07%2E)
9. W. Dai, G.-R. Xue, Q. Yang, Y. Yu. 2007b. Co-clustering based classification for out-of-domain documents. In Proceedings of KDD-07. [find](https://scholar.google.com/scholar?q=W%2E+Dai%2C+G%2E%2DR%2E+Xue%2C+Q%2E+Yang%2C+Y%2E+Yu%2E+2007b%2E+Co%2Dclustering+based+classification+for+out%2Dof%2Ddomain+documents%2E+In+Proceedings+of+KDD%2D07%2E)
10. H. DauméIII and D. Marcu. 2006. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 26:101–126. [find](https://scholar.google.com/scholar?q=H%2E+Daum%C3%A9III+and+D%2E+Marcu%2E+2006%2E+Domain+adaptation+for+statistical+classifiers%2E+Journal+of+Artificial+Intelligence+Research%2C+26%3A101%E2%80%93126%2E)
11. A. Devitt and K. Ahmad. 2007. Sentiment polarity identification in financial news: a cohesion-based approach. In Proceedings of ACL2007. [find](https://scholar.google.com/scholar?q=A%2E+Devitt+and+K%2E+Ahmad%2E+2007%2E+Sentiment+polarity+identification+in+financial+news%3A+a+cohesion%2Dbased+approach%2E+In+Proceedings+of+ACL2007%2E)
12. T. G. Dietterich. 1997. Machine learning research: four current directions. AI Magazine, 18(4), 1997. [find](https://scholar.google.com/scholar?q=T%2E+G%2E+Dietterich%2E+1997%2E+Machine+learning+research%3A+four+current+directions%2E+AI+Magazine%2C+18%284%29%2C+1997%2E)
13. A. Gliozzo and C. Strapparava. 2005. Cross language text categorization by acquiring multilingual domain models from comparable corpora. In Proceedings of the ACL Workshop on Building and Using Parallel Texts. [find](https://scholar.google.com/scholar?q=A%2E+Gliozzo+and+C%2E+Strapparava%2E+2005%2E+Cross+language+text+categorization+by+acquiring+multilingual+domain+models+from+comparable+corpora%2E+In+Proceedings+of+the+ACL+Workshop+on+Building+and+Using+Parall)
14. K. Hiroshi, N. Tetsuya and W. Hideo. 2004. Deeper sentiment analysis using machine translation technology. In Proceedings of COLING-04. [find](https://scholar.google.com/scholar?q=K%2E+Hiroshi%2C+N%2E+Tetsuya+and+W%2E+Hideo%2E+2004%2E+Deeper+sentiment+analysis+using+machine+translation+technology%2E+In+Proceedings+of+COLING%2D04%2E)
15. J. Jiang and C. Zhai. 2007. A two-stage approach to domain adaptation for statistical classifiers. In Proceedings of CIKM-07. [find](https://scholar.google.com/scholar?q=J%2E+Jiang+and+C%2E+Zhai%2E+2007%2E+A+two%2Dstage+approach+to+domain+adaptation+for+statistical+classifiers%2E+In+Proceedings+of+CIKM%2D07%2E)
16. T. Joachims. 1999. Transductive inference for text classification using support vector machines. In Proceedings of ICML-99. [find](https://scholar.google.com/scholar?q=T%2E+Joachims%2E+1999%2E+Transductive+inference+for+text+classification+using+support+vector+machines%2E+In+Proceedings+of+ICML%2D99%2E)
17. T. Joachims. 2002. Learning to classify text using support vector machines. [find](https://scholar.google.com/scholar?q=T%2E+Joachims%2E+2002%2E+Learning+to+classify+text+using+support+vector+machines%2E)
18. Dissertation, Kluwer, 2002. [find](https://scholar.google.com/scholar?q=Dissertation%2C+Kluwer%2C+2002%2E)
19. A. Kennedy and D. Inkpen. 2006. Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence, 22(2):110-125. [find](https://scholar.google.com/scholar?q=A%2E+Kennedy+and+D%2E+Inkpen%2E+2006%2E+Sentiment+classification+of+movie+reviews+using+contextual+valence+shifters%2E+Computational+Intelligence%2C+22%282%29%3A110%2D125%2E)
20. S.-M. Kim and E. Hovy. 2004. Determining the sentiment of opinions. In Proceedings of COLING-04. [find](https://scholar.google.com/scholar?q=S%2E%2DM%2E+Kim+and+E%2E+Hovy%2E+2004%2E+Determining+the+sentiment+of+opinions%2E+In+Proceedings+of+COLING%2D04%2E) [our card](https://md.research.elamur.ai/free/en/papers/kim-2004-determining-sentiment-opinions/)
21. S. Kiritchenko and S. Matwin. 2001. Email classification with co-training. In Proceedings of the 2001 Conference of the Centre for Advanced Studies on Collaborative Research. [find](https://scholar.google.com/scholar?q=S%2E+Kiritchenko+and+S%2E+Matwin%2E+2001%2E+Email+classification+with+co%2Dtraining%2E+In+Proceedings+of+the+2001+Conference+of+the+Centre+for+Advanced+Studies+on+Collaborative+Research%2E)
22. L.-W. Ku, Y.-T. Liang and H.-H. Chen. 2006. Opinion extraction, summarization and tracking in news and blog corpora. In Proceedings of AAAI-2006. [find](https://scholar.google.com/scholar?q=L%2E%2DW%2E+Ku%2C+Y%2E%2DT%2E+Liang+and+H%2E%2DH%2E+Chen%2E+2006%2E+Opinion+extraction%2C+summarization+and+tracking+in+news+and+blog+corpora%2E+In+Proceedings+of+AAAI%2D2006%2E)
23. J. Li and M. Sun. 2007. Experimental study on sentiment classification of Chinese review using machine learning techniques. In Proceeding of IEEE-NLPKE-07. [find](https://scholar.google.com/scholar?q=J%2E+Li+and+M%2E+Sun%2E+2007%2E+Experimental+study+on+sentiment+classification+of+Chinese+review+using+machine+learning+techniques%2E+In+Proceeding+of+IEEE%2DNLPKE%2D07%2E)
24. X. Ling, W. Dai, Y. Jiang, G.-R. Xue, Q. Yang, and Y. Yu. 2008. Can Chinese Web pages be classified with English data source? In Proceedings of WWW-08. [find](https://scholar.google.com/scholar?q=X%2E+Ling%2C+W%2E+Dai%2C+Y%2E+Jiang%2C+G%2E%2DR%2E+Xue%2C+Q%2E+Yang%2C+and+Y%2E+Yu%2E+2008%2E+Can+Chinese+Web+pages+be+classified+with+English+data+source%3F+In+Proceedings+of+WWW%2D08%2E)
25. B. Liu, M. Hu and J. Cheng. 2005. Opinion observer: Analyzing and comparing opinions on the web. In Proceedings of WWW-2005. [find](https://scholar.google.com/scholar?q=B%2E+Liu%2C+M%2E+Hu+and+J%2E+Cheng%2E+2005%2E+Opinion+observer%3A+Analyzing+and+comparing+opinions+on+the+web%2E+In+Proceedings+of+WWW%2D2005%2E)
26. R. McDonald, K. Hannan, T. Neylon, M. Wells and J. Reynar. 2007. Structured models for fine-to-coarse sentiment analysis. In Proceedings of ACL-07. [find](https://scholar.google.com/scholar?q=R%2E+McDonald%2C+K%2E+Hannan%2C+T%2E+Neylon%2C+M%2E+Wells+and+J%2E+Reynar%2E+2007%2E+Structured+models+for+fine%2Dto%2Dcoarse+sentiment+analysis%2E+In+Proceedings+of+ACL%2D07%2E)
27. R. Mihalcea. 2004. Co-training and self-training for word sense disambiguation. In Proceedings of CONLL-04. [find](https://scholar.google.com/scholar?q=R%2E+Mihalcea%2E+2004%2E+Co%2Dtraining+and+self%2Dtraining+for+word+sense+disambiguation%2E+In+Proceedings+of+CONLL%2D04%2E)
28. R. Mihalcea, C. Banea and J. Wiebe. 2007. Learning multilingual subjective language via cross-lingual projections. In Proceedings of ACL-2007. [find](https://scholar.google.com/scholar?q=R%2E+Mihalcea%2C+C%2E+Banea+and+J%2E+Wiebe%2E+2007%2E+Learning+multilingual+subjective+language+via+cross%2Dlingual+projections%2E+In+Proceedings+of+ACL%2D2007%2E) [our card](https://md.research.elamur.ai/free/en/papers/mihalcea-2007-learning-multilingual-subjective-language-cross/)
29. T. Mullen and N. Collier. 2004. Sentiment analysis using support vector machines with diverse information sources. In Proceedings of EMNLP-04. [find](https://scholar.google.com/scholar?q=T%2E+Mullen+and+N%2E+Collier%2E+2004%2E+Sentiment+analysis+using+support+vector+machines+with+diverse+information+sources%2E+In+Proceedings+of+EMNLP%2D04%2E)
30. V. Ng and C. Cardie. 2003. Weakly supervised natural language learning without redundant views. In Proceedings of HLT-NAACL-03. [find](https://scholar.google.com/scholar?q=V%2E+Ng+and+C%2E+Cardie%2E+2003%2E+Weakly+supervised+natural+language+learning+without+redundant+views%2E+In+Proceedings+of+HLT%2DNAACL%2D03%2E)
31. K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell. 2000. Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2-3):103–134. [find](https://scholar.google.com/scholar?q=K%2E+Nigam%2C+A%2E+K%2E+McCallum%2C+S%2E+Thrun%2C+and+T%2E+Mitchell%2E+2000%2E+Text+Classification+from+Labeled+and+Unlabeled+Documents+using+EM%2E+Machine+Learning%2C+39%282%2D3%29%3A103%E2%80%93134%2E)
32. B. Pang, L. Lee and S. Vaithyanathan. 2002. Thumbs up? sentiment classification using machine learning techniques. In Proceedings of EMNLP-02. [find](https://scholar.google.com/scholar?q=B%2E+Pang%2C+L%2E+Lee+and+S%2E+Vaithyanathan%2E+2002%2E+Thumbs+up%3F+sentiment+classification+using+machine+learning+techniques%2E+In+Proceedings+of+EMNLP%2D02%2E) [our card](https://md.research.elamur.ai/free/en/papers/pang-2002-thumbs-up-sentiment-classification-machine/)
33. B. Pang and L. Lee. 2004. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of ACL-04. [find](https://scholar.google.com/scholar?q=B%2E+Pang+and+L%2E+Lee%2E+2004%2E+A+sentimental+education%3A+sentiment+analysis+using+subjectivity+summarization+based+on+minimum+cuts%2E+In+Proceedings+of+ACL%2D04%2E) [our card](https://md.research.elamur.ai/free/en/papers/pang-2004-sentimental-education-sentiment-analysis-subjectivity/)
34. J. Read. 2005. Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In Proceedings of ACL-05. [find](https://scholar.google.com/scholar?q=J%2E+Read%2E+2005%2E+Using+emoticons+to+reduce+dependency+in+machine+learning+techniques+for+sentiment+classification%2E+In+Proceedings+of+ACL%2D05%2E)
35. L. Rigutini, M. Maggini and B. Liu. 2005. An EM based training algorithm for cross-language text categorization. In Proceedings of WI-05. [find](https://scholar.google.com/scholar?q=L%2E+Rigutini%2C+M%2E+Maggini+and+B%2E+Liu%2E+2005%2E+An+EM+based+training+algorithm+for+cross%2Dlanguage+text+categorization%2E+In+Proceedings+of+WI%2D05%2E)
36. A. Sarkar. 2001. Applying cotraining methods to statistical parsing. In Proceedings of NAACL-2001. [find](https://scholar.google.com/scholar?q=A%2E+Sarkar%2E+2001%2E+Applying+cotraining+methods+to+statistical+parsing%2E+In+Proceedings+of+NAACL%2D2001%2E)
37. I. Titov and R. McDonald. 2008. A joint model of text and aspect ratings for sentiment summarization. In Proceedings of ACL-08:HLT. [find](https://scholar.google.com/scholar?q=I%2E+Titov+and+R%2E+McDonald%2E+2008%2E+A+joint+model+of+text+and+aspect+ratings+for+sentiment+summarization%2E+In+Proceedings+of+ACL%2D08%3AHLT%2E) [our card](https://md.research.elamur.ai/free/en/papers/titov-2008-joint-model-text-aspect-ratings/)
38. B. K. Y. Tsou, R. W. M. Yuen, O. Y. Kwong, T. B. Y. La and W. L. Wong. 2005. Polarity classification of celebrity coverage in the Chinese press. In Proceedings of International Conference on Intelligence Analysis. [find](https://scholar.google.com/scholar?q=B%2E+K%2E+Y%2E+Tsou%2C+R%2E+W%2E+M%2E+Yuen%2C+O%2E+Y%2E+Kwong%2C+T%2E+B%2E+Y%2E+La+and+W%2E+L%2E+Wong%2E+2005%2E+Polarity+classification+of+celebrity+coverage+in+the+Chinese+press%2E+In+Proceedings+of+International+Conference+on+Intellige)
39. P. Turney. 2002. Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In Proceedings of ACL-2002. [find](https://scholar.google.com/scholar?q=P%2E+Turney%2E+2002%2E+Thumbs+up+or+thumbs+down%3F+semantic+orientation+applied+to+unsupervised+classification+of+reviews%2E+In+Proceedings+of+ACL%2D2002%2E) [our card](https://md.research.elamur.ai/free/en/papers/turney-2002-thumbs-up-or-thumbs-down/)
40. X. Wan. 2008. Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In Proceedings of EMNLP-2008. [find](https://scholar.google.com/scholar?q=X%2E+Wan%2E+2008%2E+Using+bilingual+knowledge+and+ensemble+techniques+for+unsupervised+Chinese+sentiment+analysis%2E+In+Proceedings+of+EMNLP%2D2008%2E)
41. T. Wilson, J. Wiebe and P. Hoffmann. 2005. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In Proceedings of HLT/EMNLP-05. [find](https://scholar.google.com/scholar?q=T%2E+Wilson%2C+J%2E+Wiebe+and+P%2E+Hoffmann%2E+2005%2E+Recognizing+Contextual+Polarity+in+Phrase%2DLevel+Sentiment+Analysis%2E+In+Proceedings+of+HLT%2FEMNLP%2D05%2E) [our card](https://md.research.elamur.ai/free/en/papers/wilson-2005-recognizing-contextual-polarity-phrase-level/)
42. G.-R. Xue, W. Dai, Q. Yang, Y. Yu. 2008. Topic-bridged PLSA for cross-domain text classification. In Proceedings of SIGIR-08. [find](https://scholar.google.com/scholar?q=G%2E%2DR%2E+Xue%2C+W%2E+Dai%2C+Q%2E+Yang%2C+Y%2E+Yu%2E+2008%2E+Topic%2Dbridged+PLSA+for+cross%2Ddomain+text+classification%2E+In+Proceedings+of+SIGIR%2D08%2E)
43. Q. Ye, W. Shi and Y. Li. 2006. Sentiment classification for movie reviews in Chinese by improved semantic oriented approach. In Proceedings of 39th Hawaii International Conference on System Sciences, 2006. [find](https://scholar.google.com/scholar?q=Q%2E+Ye%2C+W%2E+Shi+and+Y%2E+Li%2E+2006%2E+Sentiment+classification+for+movie+reviews+in+Chinese+by+improved+semantic+oriented+approach%2E+In+Proceedings+of+39th+Hawaii+International+Conference+on+System+Sciences%2C+)