---
title: "Semi-Supervised Recursive Autoencoders for — Socher 2011"
description: "Most sentiment classifiers of the time relied on bag-of-words features, which ignore word order and composition."
authors: ["Richard Socher", "Jeffrey Pennington", "Eric H. Huang", "Andrew Y. Ng", "Christopher D. Manning"]
year: 2011
venue: "Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D11-1014/"
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/socher-2011-semi-supervised-recursive-autoencoders-predicting/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/socher-2011-semi-supervised-recursive-autoencoders-predicting/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2011](https://md.research.elamur.ai/free/en/papers/2011/) / Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions

# Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions

**Authors:** [Richard Socher](https://md.research.elamur.ai/free/en/authors/richard-socher/), Jeffrey Pennington, Eric H. Huang, [Andrew Y. Ng](https://md.research.elamur.ai/free/en/authors/andrew-y-ng/), [Christopher D. Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/) · **Year:** 2011 · **Venue:** Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing · **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/D11-1014/) · [PDF](https://aclanthology.org/D11-1014.pdf) · [OpenAlex](https://openalex.org/W71795751)

**Citations:** 1150 citations (openalex-snapshot · 2026-10-14) · [261](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Most sentiment classifiers of the time relied on bag-of-words features, which ignore word order and composition. Systems that did model composition depended on hand-built resources such as sentiment lexica, parsers and polarity-shifting rules. The authors propose semi-supervised recursive autoencoders (RAE) to address both limitations. The model starts from continuous word vectors, either randomly initialized or pre-trained with a neural language model. It repeatedly merges neighbouring vectors into phrase vectors, greedily choosing the pair that reconstructs its children best, which yields a tree and a fixed-length sentence representation without any parser. A softmax layer on each node is trained jointly with the reconstruction objective to predict a distribution over sentiment labels. The model is tested on standard benchmarks such as movie reviews and on a new experience project (EP) corpus, where readers react to confessions with five possible labels. The paper reports state-of-the-art accuracy on the standard data, outperforming the CRF system of Nakagawa et al. (2010) without external tools. On EP it predicts reaction distributions better than competitive baselines. The work matters because it learns compositional, multi-dimensional sentiment with little language-specific engineering.

## Key points
- The RAE reuses one autoencoder at every tree node and builds structure greedily from reconstruction error. A CKY-like beam search performed similarly but was much slower.
- No sentiment lexicon, POS tagger, dependency parser or polarity rules are needed. The model learns word and n-gram polarity as a by-product.
- The EP dataset (Potts, 2010) contains over 31,000 confessions released together with the code at www.socher.org. Readers can respond with one of five reactions: "Sorry, hugs", "You rock", "Wow, just wow", "Tehee" or "I understand".
- On EP, the model is evaluated on two tasks: predicting the top-voted label and predicting the full label distribution. It beats the baselines on both.
- The authors argue that the learned trees capture semantic content of the word vectors even when they are not syntactically plausible. This sets the approach apart from the supervised RNNs in Socher et al. (2010, 2011).

*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

261 papers of our library cite it (of the 140673 whose bibliographies we read); 32 of them have a page here, 229 not yet.

- [Relational inductive biases, deep learning, and graph networks](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/)
- [Representation Learning: A Review and New Perspectives](https://md.research.elamur.ai/free/en/papers/bengio-2012-representation-learning-review-new-perspectives/)
- [A Comparison of Vector-based Representations for Semantic Composition](https://md.research.elamur.ai/free/en/papers/blacoe-2012-comparison-vector-based-representations-semantic/)
- [Recurrent Attention Network on Memory for Aspect Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/chen-2017-recurrent-attention-network-memory-aspect/)
- [Very Deep Convolutional Networks for Text Classification](https://md.research.elamur.ai/free/en/papers/conneau-2017-very-deep-convolutional-networks-text/)
- [Learning Semantic Hierarchies via Word Embeddings](https://md.research.elamur.ai/free/en/papers/fu-2014-learning-semantic-hierarchies-word-embeddings/)
- [Fracking Sarcasm using Neural Network](https://md.research.elamur.ai/free/en/papers/ghosh-2016-fracking-sarcasm-neural-network/)
- [Multilingual Models for Compositional Distributed Semantics](https://md.research.elamur.ai/free/en/papers/hermann-2014-multilingual-models-compositional-distributed-semantics/)
- [Learning Distributed Representations of Sentences from Unlabelled Data](https://md.research.elamur.ai/free/en/papers/hill-2016-learning-distributed-representations-sentences-unlabelled/)
- [Improving Word Representations via Global Context and Multiple Word Prototypes](https://md.research.elamur.ai/free/en/papers/huang-2012-improving-word-representations-global-context/)
- [Political Ideology Detection Using Recursive Neural Networks](https://md.research.elamur.ai/free/en/papers/iyyer-2014-political-ideology-detection-recursive-neural/)
- [Deep Unordered Composition Rivals Syntactic Methods for Text Classification](https://md.research.elamur.ai/free/en/papers/iyyer-2015-deep-unordered-composition-rivals-syntactic/)
- [A Convolutional Neural Network for Modelling Sentences](https://md.research.elamur.ai/free/en/papers/kalchbrenner-2014-convolutional-neural-network-modelling-sentences/)
- [Convolutional Neural Networks for Sentence Classification](https://md.research.elamur.ai/free/en/papers/kim-2014-convolutional-neural-networks-sentence-classification/)
- [Inducing Crosslingual Distributed Representations of Words](https://md.research.elamur.ai/free/en/papers/klementiev-2012-inducing-crosslingual-distributed-representations-words/)
- [Distributed Representations of Sentences and Documents](https://md.research.elamur.ai/free/en/papers/le-2014-distributed-representations-sentences-documents/)
- [Linguistic Regularities in Sparse and Explicit Word Representations](https://md.research.elamur.ai/free/en/papers/levy-2014-linguistic-regularities-sparse-explicit-word/)
- [A Structured Self-attentive Sentence Embedding](https://md.research.elamur.ai/free/en/papers/lin-2017-structured-self-attentive-sentence-embedding/)
- [Better Word Representations with Recursive Neural Networks for Morphology](https://md.research.elamur.ai/free/en/papers/luong-2013-better-word-representations-recursive-neural/)
- [context2vec: Learning Generic Context Embedding with Bidirectional LSTM](https://md.research.elamur.ai/free/en/papers/melamud-2016-context2vec-learning-generic-context-embedding/)
- [Natural Language Inference by Tree-Based Convolution and Heuristic Matching](https://md.research.elamur.ai/free/en/papers/mou-2016-natural-language-inference-tree-based/)
- [Learning Joint Multilingual Sentence Representations with Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/schwenk-2017-learning-joint-multilingual-sentence-representations/)
- [Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms](https://md.research.elamur.ai/free/en/papers/shen-2018-baseline-needs-more-love-simple/)
- [Semantic Compositionality through Recursive Matrix-Vector Spaces](https://md.research.elamur.ai/free/en/papers/socher-2012-semantic-compositionality-through-recursive-matrix/)
- [Grounded Compositional Semantics for Finding and Describing Images with Sentences](https://md.research.elamur.ai/free/en/papers/socher-2014-grounded-compositional-semantics-finding-describing/)
- [Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification](https://md.research.elamur.ai/free/en/papers/tang-2014-learning-sentiment-specific-word-embedding/)
- [Baselines and Bigrams: Simple, Good Sentiment and Topic Classification](https://md.research.elamur.ai/free/en/papers/wang-2012-baselines-bigrams-simple-good-sentiment/)
- [Attention-based LSTM for Aspect-level Sentiment Classification](https://md.research.elamur.ai/free/en/papers/wang-2016-attention-based-lstm-aspect-level/)
- [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/)
- [Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/wiseman-2015-learning-anaphoricity-antecedent-ranking-features/)
- [Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths](https://md.research.elamur.ai/free/en/papers/xu-2015-classifying-relations-long-short-term/)
- [Bilingual Word Embeddings for Phrase-Based Machine Translation](https://md.research.elamur.ai/free/en/papers/zou-2013-bilingual-word-embeddings-phrase-based/)

## Cite

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

```bibtex
@inproceedings{socher2011semi,
  title = {{Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions}},
  author = {Socher, Richard and Pennington, Jeffrey and Huang, Eric H. and Ng, Andrew Y. and Manning, Christopher D.},
  booktitle = {Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing},
  year = {2011},
  url = {https://aclanthology.org/D11-1014/}
}
```

- [← previous Semi-Supervised Frame-Semantic Parsing for Unknown Predicates](https://md.research.elamur.ai/free/en/papers/das-2011-semi-supervised-frame-semantic-parsing/)
- [next → Sentiment Analysis of Twitter Data](https://md.research.elamur.ai/free/en/papers/agarwal-2011-sentiment-analysis-twitter-data/)
- [↑ back to papers of 2011](https://md.research.elamur.ai/free/en/papers/2011/)

## References

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

1. Beineke, T. Hastie, C. D. Manning, and S. Vaithyanathan. 2004. Exploring sentiment summarization. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications. [find](https://scholar.google.com/scholar?q=Beineke%2C+T%2E+Hastie%2C+C%2E+D%2E+Manning%2C+and+S%2E+Vaithyanathan%2E+2004%2E+Exploring+sentiment+summarization%2E+In+Proceedings+of+the+AAAI+Spring+Symposium+on+Exploring+Attitude+and+Affect+in+Text%3A+Theories+and+App)
2. Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin. 2003. A neural probabilistic language model. Journal of Machine Learning Research, 3:1137–1155. [find](https://scholar.google.com/scholar?q=Y%2E+Bengio%2C+R%2E+Ducharme%2C+P%2E+Vincent%2C+and+C%2E+Janvin%2E+2003%2E+A+neural+probabilistic+language+model%2E+Journal+of+Machine+Learning+Research%2C+3%3A1137%E2%80%931155%2E)
3. D. M. Blei, A. Y. Ng, and M. I. Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research., 3:993–1022. [find](https://scholar.google.com/scholar?q=D%2E+M%2E+Blei%2C+A%2E+Y%2E+Ng%2C+and+M%2E+I%2E+Jordan%2E+2003%2E+Latent+dirichlet+allocation%2E+Journal+of+Machine+Learning+Research%2E%2C+3%3A993%E2%80%931022%2E) [our card](https://md.research.elamur.ai/free/en/papers/blei-2003-latent-dirichlet-allocation/)
4. Y. Choi and C. Cardie. 2008. Learning with compositional semantics as structural inference for subsentential sentiment analysis. In EMNLP. [find](https://scholar.google.com/scholar?q=Y%2E+Choi+and+C%2E+Cardie%2E+2008%2E+Learning+with+compositional+semantics+as+structural+inference+for+subsentential+sentiment+analysis%2E+In+EMNLP%2E)
5. R. Collobert and J. Weston. 2008. A unified architecture for natural language processing: deep neural networks with multitask learning. In Proceedings of ICML, pages 160–167. [find](https://scholar.google.com/scholar?q=R%2E+Collobert+and+J%2E+Weston%2E+2008%2E+A+unified+architecture+for+natural+language+processing%3A+deep+neural+networks+with+multitask+learning%2E+In+Proceedings+of+ICML%2C+pages+160%E2%80%93167%2E)
6. S. Das and M. Chen. 2001. Yahoo! for Amazon: Extracting market sentiment from stock message boards. In Proceedings of the Asia Pacific Finance Association Annual Conference (APFA). [find](https://scholar.google.com/scholar?q=S%2E+Das+and+M%2E+Chen%2E+2001%2E+Yahoo%21+for+Amazon%3A+Extracting+market+sentiment+from+stock+message+boards%2E+In+Proceedings+of+the+Asia+Pacific+Finance+Association+Annual+Conference+%28APFA%29%2E)
7. K. Dave, S. Lawrence, and D. M. Pennock. 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of WWW, pages 519–528. [find](https://scholar.google.com/scholar?q=K%2E+Dave%2C+S%2E+Lawrence%2C+and+D%2E+M%2E+Pennock%2E+2003%2E+Mining+the+peanut+gallery%3A+Opinion+extraction+and+semantic+classification+of+product+reviews%2E+In+Proceedings+of+WWW%2C+pages+519%E2%80%93528%2E)
8. X. Ding, B. Liu, and P. S. Yu. 2008. A holistic lexicon-based approach to opinion mining. In Proceedings of the Conference on Web Search and Web Data Mining (WSDM). [find](https://scholar.google.com/scholar?q=X%2E+Ding%2C+B%2E+Liu%2C+and+P%2E+S%2E+Yu%2E+2008%2E+A+holistic+lexicon%2Dbased+approach+to+opinion+mining%2E+In+Proceedings+of+the+Conference+on+Web+Search+and+Web+Data+Mining+%28WSDM%29%2E)
9. J. L. Elman. 1991. Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7(2-3):195–225. [find](https://scholar.google.com/scholar?q=J%2E+L%2E+Elman%2E+1991%2E+Distributed+representations%2C+simple+recurrent+networks%2C+and+grammatical+structure%2E+Machine+Learning%2C+7%282%2D3%29%3A195%E2%80%93225%2E)
10. A. Esuli and F. Sebastiani. 2007. Pageranking wordnet synsets: An application to opinion mining. In Proceedings of the Association for Computational Linguistics (ACL). [find](https://scholar.google.com/scholar?q=A%2E+Esuli+and+F%2E+Sebastiani%2E+2007%2E+Pageranking+wordnet+synsets%3A+An+application+to+opinion+mining%2E+In+Proceedings+of+the+Association+for+Computational+Linguistics+%28ACL%29%2E)
11. C. Goller and A. Küchler. 1996. Learning task-dependent distributed representations by backpropagation through structure. In Proceedings of the International Conference on Neural Networks (ICNN-96). [find](https://scholar.google.com/scholar?q=C%2E+Goller+and+A%2E+Ku%CC%88chler%2E+1996%2E+Learning+task%2Ddependent+distributed+representations+by+backpropagation+through+structure%2E+In+Proceedings+of+the+International+Conference+on+Neural+Networks+%28ICNN%2D96%29%2E)
12. G. Grefenstette, Y. Qu, J. G. Shanahan, and D. A. Evans. 2004. Coupling niche browsers and affect analysis for an opinion mining application. In Proceedings of Recherche d’Information Assistée par Ordinateur (RIAO). [find](https://scholar.google.com/scholar?q=G%2E+Grefenstette%2C+Y%2E+Qu%2C+J%2E+G%2E+Shanahan%2C+and+D%2E+A%2E+Evans%2E+2004%2E+Coupling+niche+browsers+and+affect+analysis+for+an+opinion+mining+application%2E+In+Proceedings+of+Recherche+d%E2%80%99Information+Assiste%CC%81e+par+Or)
13. D. Ikeda, H. Takamura, L. Ratinov, and M. Okumura. 2008. Learning to shift the polarity of words for sentiment classification. In IJCNLP. [find](https://scholar.google.com/scholar?q=D%2E+Ikeda%2C+H%2E+Takamura%2C+L%2E+Ratinov%2C+and+M%2E+Okumura%2E+2008%2E+Learning+to+shift+the+polarity+of+words+for+sentiment+classification%2E+In+IJCNLP%2E)
14. S. Kim and E. Hovy. 2007. Crystal: Analyzing predictive opinions on the web. In EMNLP-CoNLL. [find](https://scholar.google.com/scholar?q=S%2E+Kim+and+E%2E+Hovy%2E+2007%2E+Crystal%3A+Analyzing+predictive+opinions+on+the+web%2E+In+EMNLP%2DCoNLL%2E)
15. A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts. 2011. Learning accurate, compact, and interpretable tree annotation. In Proceedings of ACL. [find](https://scholar.google.com/scholar?q=A%2E+L%2E+Maas%2C+R%2E+E%2E+Daly%2C+P%2E+T%2E+Pham%2C+D%2E+Huang%2C+A%2E+Y%2E+Ng%2C+and+C%2E+Potts%2E+2011%2E+Learning+accurate%2C+compact%2C+and+interpretable+tree+annotation%2E+In+Proceedings+of+ACL%2E)
16. Y. Mao and G. Lebanon. 2007. Isotonic Conditional Random Fields and Local Sentiment Flow. In NIPS. [find](https://scholar.google.com/scholar?q=Y%2E+Mao+and+G%2E+Lebanon%2E+2007%2E+Isotonic+Conditional+Random+Fields+and+Local+Sentiment+Flow%2E+In+NIPS%2E)
17. P. Mirowski, M. Ranzato, and Y. LeCun. 2010. Dynamic auto-encoders for semantic indexing. In Proceedings of the NIPS 2010 Workshop on Deep Learning. [find](https://scholar.google.com/scholar?q=P%2E+Mirowski%2C+M%2E+Ranzato%2C+and+Y%2E+LeCun%2E+2010%2E+Dynamic+auto%2Dencoders+for+semantic+indexing%2E+In+Proceedings+of+the+NIPS+2010+Workshop+on+Deep+Learning%2E)
18. T. Nakagawa, K. Inui, and S. Kurohashi. 2010. Dependency tree-based sentiment classification using CRFs with hidden variables. In NAACL, HLT. [find](https://scholar.google.com/scholar?q=T%2E+Nakagawa%2C+K%2E+Inui%2C+and+S%2E+Kurohashi%2E+2010%2E+Dependency+tree%2Dbased+sentiment+classification+using+CRFs+with+hidden+variables%2E+In+NAACL%2C+HLT%2E)
19. B. Pang and L. Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In ACL. [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+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/pang-2004-sentimental-education-sentiment-analysis-subjectivity/)
20. B. Pang and L. Lee. 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In ACL, pages 115–124. [find](https://scholar.google.com/scholar?q=B%2E+Pang+and+L%2E+Lee%2E+2005%2E+Seeing+stars%3A+Exploiting+class+relationships+for+sentiment+categorization+with+respect+to+rating+scales%2E+In+ACL%2C+pages+115%E2%80%93124%2E) [our card](https://md.research.elamur.ai/free/en/papers/pang-2005-seeing-stars-exploiting-class-relationships/)
21. B. Pang and L. Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2):1–135. [find](https://scholar.google.com/scholar?q=B%2E+Pang+and+L%2E+Lee%2E+2008%2E+Opinion+mining+and+sentiment+analysis%2E+Foundations+and+Trends+in+Information+Retrieval%2C+2%281%2D2%29%3A1%E2%80%93135%2E)
22. B. Pang, L. Lee, and S. Vaithyanathan. 2002. Thumbs up? Sentiment classification using machine learning techniques. In EMNLP. [find](https://scholar.google.com/scholar?q=B%2E+Pang%2C+L%2E+Lee%2C+and+S%2E+Vaithyanathan%2E+2002%2E+Thumbs+up%3F+Sentiment+classification+using+machine+learning+techniques%2E+In+EMNLP%2E) [our card](https://md.research.elamur.ai/free/en/papers/pang-2002-thumbs-up-sentiment-classification-machine/)
23. J. W. Pennebaker, R.J. Booth, and M. E. Francis. 2007. Linguistic inquiry and word count: Liwc2007 operators manual. University of Texas. [find](https://scholar.google.com/scholar?q=J%2E+W%2E+Pennebaker%2C+R%2EJ%2E+Booth%2C+and+M%2E+E%2E+Francis%2E+2007%2E+Linguistic+inquiry+and+word+count%3A+Liwc2007+operators+manual%2E+University+of+Texas%2E)
24. L. Polanyi and A. Zaenen. 2006. Contextual valence shifters. [find](https://scholar.google.com/scholar?q=L%2E+Polanyi+and+A%2E+Zaenen%2E+2006%2E+Contextual+valence+shifters%2E)
25. J. B. Pollack. 1990. Recursive distributed representations. Artificial Intelligence, 46:77–105, November. [find](https://scholar.google.com/scholar?q=J%2E+B%2E+Pollack%2E+1990%2E+Recursive+distributed+representations%2E+Artificial+Intelligence%2C+46%3A77%E2%80%93105%2C+November%2E)
26. C. Potts. 2010. On the negativity of negation. In David Lutz and Nan Li, editors, Proceedings of Semantics and Linguistic Theory 20. CLC Publications, Ithaca, NY. [find](https://scholar.google.com/scholar?q=C%2E+Potts%2E+2010%2E+On+the+negativity+of+negation%2E+In+David+Lutz+and+Nan+Li%2C+editors%2C+Proceedings+of+Semantics+and+Linguistic+Theory+20%2E+CLC+Publications%2C+Ithaca%2C+NY%2E)
27. B. Snyder and R. Barzilay. 2007. Multiple aspect ranking using the Good Grief algorithm. In HLT-NAACL. [find](https://scholar.google.com/scholar?q=B%2E+Snyder+and+R%2E+Barzilay%2E+2007%2E+Multiple+aspect+ranking+using+the+Good+Grief+algorithm%2E+In+HLT%2DNAACL%2E)
28. R. Socher, C. D. Manning, and A. Y. Ng. 2010. Learning continuous phrase representations and syntactic parsing with recursive neural networks. In Proceedings of the NIPS-2010 Deep Learning and Unsupervised Feature Learning Workshop. [find](https://scholar.google.com/scholar?q=R%2E+Socher%2C+C%2E+D%2E+Manning%2C+and+A%2E+Y%2E+Ng%2E+2010%2E+Learning+continuous+phrase+representations+and+syntactic+parsing+with+recursive+neural+networks%2E+In+Proceedings+of+the+NIPS%2D2010+Deep+Learning+and+Unsuper)
29. R. Socher, C. C. Lin, A. Y. Ng, and C. D. Manning. 2011. Parsing Natural Scenes and Natural Language with Recursive Neural Networks. In ICML. [find](https://scholar.google.com/scholar?q=R%2E+Socher%2C+C%2E+C%2E+Lin%2C+A%2E+Y%2E+Ng%2C+and+C%2E+D%2E+Manning%2E+2011%2E+Parsing+Natural+Scenes+and+Natural+Language+with+Recursive+Neural+Networks%2E+In+ICML%2E)
30. P. J. Stone. 1966. The General Inquirer: A Computer Approach to Content Analysis. The MIT Press. [find](https://scholar.google.com/scholar?q=P%2E+J%2E+Stone%2E+1966%2E+The+General+Inquirer%3A+A+Computer+Approach+to+Content+Analysis%2E+The+MIT+Press%2E)
31. J. Turian, L. Ratinov, and Y. Bengio. 2010. Word representations: a simple and general method for semi-supervised learning. In Proceedings of ACL, pages 384–394. [find](https://scholar.google.com/scholar?q=J%2E+Turian%2C+L%2E+Ratinov%2C+and+Y%2E+Bengio%2E+2010%2E+Word+representations%3A+a+simple+and+general+method+for+semi%2Dsupervised+learning%2E+In+Proceedings+of+ACL%2C+pages+384%E2%80%93394%2E) [our card](https://md.research.elamur.ai/free/en/papers/turian-2010-word-representations-simple-general-method/)
32. P. Turney. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In ACL. [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+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/turney-2002-thumbs-up-or-thumbs-down/)
33. L. Velikovich, S. Blair-Goldensohn, K. Hannan, and R. McDonald. 2010. The viability of web-derived polarity lexicons. In NAACL, HLT. [find](https://scholar.google.com/scholar?q=L%2E+Velikovich%2C+S%2E+Blair%2DGoldensohn%2C+K%2E+Hannan%2C+and+R%2E+McDonald%2E+2010%2E+The+viability+of+web%2Dderived+polarity+lexicons%2E+In+NAACL%2C+HLT%2E)
34. T. Voegtlin and P. Dominey. 2005. Linear Recursive Distributed Representations. Neural Networks, 18(7). [find](https://scholar.google.com/scholar?q=T%2E+Voegtlin+and+P%2E+Dominey%2E+2005%2E+Linear+Recursive+Distributed+Representations%2E+Neural+Networks%2C+18%287%29%2E)
35. J. Wiebe, T. Wilson, and C. Cardie. 2005. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 39. [find](https://scholar.google.com/scholar?q=J%2E+Wiebe%2C+T%2E+Wilson%2C+and+C%2E+Cardie%2E+2005%2E+Annotating+expressions+of+opinions+and+emotions+in+language%2E+Language+Resources+and+Evaluation%2C+39%2E)
36. T. Wilson, J. Wiebe, and P. Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In HLT/EMNLP. [find](https://scholar.google.com/scholar?q=T%2E+Wilson%2C+J%2E+Wiebe%2C+and+P%2E+Hoffmann%2E+2005%2E+Recognizing+contextual+polarity+in+phrase%2Dlevel+sentiment+analysis%2E+In+HLT%2FEMNLP%2E) [our card](https://md.research.elamur.ai/free/en/papers/wilson-2005-recognizing-contextual-polarity-phrase-level/)
37. H. Yu and V. Hatzivassiloglou. 2003. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In EMNLP. [find](https://scholar.google.com/scholar?q=H%2E+Yu+and+V%2E+Hatzivassiloglou%2E+2003%2E+Towards+answering+opinion+questions%3A+Separating+facts+from+opinions+and+identifying+the+polarity+of+opinion+sentences%2E+In+EMNLP%2E) [our card](https://md.research.elamur.ai/free/en/papers/yu-2003-answering-opinion-questions-separating-facts/)