---
title: "Learning Sentiment-Specific Word Embedding for — Tang 2014"
description: "Standard word embeddings such as C&W and word2vec are trained only on surrounding context."
authors: ["Duyu Tang", "Furu Wei", "Nan Yang", "Ming Zhou", "Ting Liu", "Bing Qin"]
year: 2014
venue: "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P14-1146/"
doi: "10.3115/v1/p14-1146"
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/tang-2014-learning-sentiment-specific-word-embedding/"
---
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# Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification

**Authors:** [Duyu Tang](https://md.research.elamur.ai/free/en/authors/duyu-tang/), [Furu Wei](https://md.research.elamur.ai/free/en/authors/furu-wei/), [Nan Yang](https://md.research.elamur.ai/free/en/authors/nan-yang/), [Ming Zhou](https://md.research.elamur.ai/free/en/authors/ming-zhou/), [Ting Liu](https://md.research.elamur.ai/free/en/authors/ting-liu/), [Bing Qin](https://md.research.elamur.ai/free/en/authors/bing-qin/) · **Year:** 2014 · **Venue:** Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **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/P14-1146/) · [PDF](https://aclanthology.org/P14-1146.pdf) · [DOI 10.3115/v1/p14-1146](https://doi.org/10.3115/v1/p14-1146) · [OpenAlex](https://openalex.org/W2250879510)

**Citations:** 1160 citations (openalex-snapshot · 2026-10-14) · [181](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Standard word embeddings such as C&W and word2vec are trained only on surrounding context. As a result, words like *good* and *bad*, which appear in similar positions but carry opposite polarity, end up as near neighbours. That is a poor fit for sentiment analysis.

Tang et al. address this by learning sentiment-specific word embedding (SSWE). They extend the C&W neural architecture into three variants (SSWE h, SSWE r, SSWE u) whose loss functions also use the polarity of the text an ngram comes from. Labels are not annotated by hand. They come from distant supervision: about 10 million tweets selected by positive or negative emoticons.

The embeddings serve as features in a supervised classifier on the SemEval 2013 Twitter benchmark. Used alone, SSWE roughly matches the strongest hand-engineered system. Combined with that system's features, it sets a new best result.

A separate intrinsic test checks polarity agreement among nearest neighbours in sentiment lexicons, and SSWE again beats the generic embeddings. The work shows that cheap, noisy labels can replace much manual feature engineering.

## Key points
- On positive/negative SemEval 2013 classification, SSWE features alone reach 84.89% macro-F1. The top system (Mohammad et al., 2013, built on lexicons and hand-crafted features) reaches 84.70%. Concatenating both feature sets gives 86.58%.
- Training data is about 10 million emoticon-labelled tweets. Development-set macro-F1 for SSWE u rises with more data, reaching 82.94% at 10M, and stays flat beyond that. The learned embeddings are released publicly.
- SSWE u, the unified model combining syntactic context with sentence sentiment, is the best of the three variants. SSWE h and SSWE r perform about equally.
- Adding bigram and trigram embeddings consistently improves classification. This helps with phrases whose polarity differs from that of their parts, such as *not bad*.
- In the lexicon test (top-100 neighbours, cosine similarity), SSWE u scores 77.30 on HL, 71.74 on MPQA and 77.33 on the Joint lexicon. Word2vec scores 66.22, 60.72 and 65.59, and C&W scores 63.10, 58.13 and 62.58. A random baseline scores 50.

## Interesting ideas
- **Noisy labels as a training signal for representations, not for the classifier.** Emoticon-labelled tweets are too noisy to train a final classifier directly, but they are good enough to shape embeddings. The same trick could apply to any domain with cheap proxy labels, such as star ratings, hashtags or reactions.
- **Mixing a context objective with a task objective in one loss.** The best model keeps the C&W-style context signal and adds a sentiment signal alongside it. Doing this preserves general word knowledge while injecting task-relevant distinctions, which is a reusable pattern for domain-tailored embeddings.
- **Embedding ngrams as units.** Learning separate vectors for bigrams and trigrams captures negations and idioms whose meaning is not compositional. This is a lightweight alternative to full compositional models.
- **Intrinsic check via neighbour polarity agreement.** Measuring how often a lexicon word's nearest neighbours share its label is a cheap, interpretable diagnostic. It could be adapted to any labelled word list, such as emotion or toxicity lexicons, including for Ukrainian resources.
- **Data-size curve to find saturation.** Plotting dev performance against corpus size showed where extra data stops helping. This is useful for budgeting collection effort in new languages or domains.

*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

181 papers of our library cite it (of the 140884 whose bibliographies we read); 10 of them have a page here, 171 not yet.

- [Effect of Word Embedding Variable Parameters on Arabic Sentiment Analysis Performance](https://md.research.elamur.ai/free/en/papers/alnawas-2021-effect-word-embedding-variable-parameters/)
- [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://md.research.elamur.ai/free/en/papers/felbo-2017-millions-emoji-occurrences-learn-any/)
- [Cross-Domain Label-Adaptive Stance Detection](https://md.research.elamur.ai/free/en/papers/hardalov-2021-cross-domain-label-adaptive-stance/)
- [Demographic Factors Improve Classification Performance](https://md.research.elamur.ai/free/en/papers/hovy-2015-demographic-factors-improve-classification-performance/)
- [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/johnson-2015-effective-use-word-order-text/)
- [EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/mageed-2017-emonet-fine-grained-emotion-detection/)
- [Document Modeling with Gated Recurrent Neural Network for Sentiment Classification](https://md.research.elamur.ai/free/en/papers/tang-2015-document-modeling-gated-recurrent-neural/)
- [Effective LSTMs for Target-Dependent Sentiment Classification](https://md.research.elamur.ai/free/en/papers/tang-2016-effective-lstms-target-dependent-sentiment/)
- [pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection](https://md.research.elamur.ai/free/en/papers/wei-2016-pkudblab-semeval-2016-task-6/)
- [Hierarchical Attention Networks for Document Classification](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/)

## Cite

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

```bibtex
@inproceedings{tang2014learning,
  title = {{Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification}},
  author = {Tang, Duyu and Wei, Furu and Yang, Nan and Zhou, Ming and Liu, Ting and Qin, Bing},
  booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2014},
  doi = {10.3115/v1/p14-1146},
  url = {https://doi.org/10.3115/v1/p14-1146}
}
```

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