---
title: "Attention-based LSTM for Aspect-level Sentiment — Wang 2016"
description: "The paper addresses aspect-level sentiment classification, where a single sentence can carry different polarities toward different aspects, for example…"
authors: ["Yequan Wang", "Minlie Huang", "Xiaoyan Zhu", "Li Zhao"]
year: 2016
venue: "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D16-1058/"
doi: "10.18653/v1/d16-1058"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2016-attention-based-lstm-aspect-level/"
---
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# Attention-based LSTM for Aspect-level Sentiment Classification

**Authors:** [Yequan Wang](https://md.research.elamur.ai/free/en/authors/yequan-wang/), [Minlie Huang](https://md.research.elamur.ai/free/en/authors/minlie-huang/), Xiaoyan Zhu, Li Zhao · **Year:** 2016 · **Venue:** Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D16-1058/) · [PDF](https://aclanthology.org/D16-1058.pdf) · [DOI 10.18653/v1/d16-1058](https://doi.org/10.18653/v1/d16-1058) · [OpenAlex](https://openalex.org/W2562607067)

**Citations:** 2348 citations (openalex-snapshot · 2026-10-14) · [261](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses aspect-level sentiment classification, where a single sentence can carry different polarities toward different aspects, for example praise for food alongside criticism of service. Earlier neural approaches such as TD-LSTM and TC-LSTM model a target phrase but do not use explicit aspect information. The authors give each aspect its own learned embedding and combine it with an LSTM through an attention layer, so the model weighs words differently depending on which aspect is being asked about. They propose three variants:
- **AE-LSTM** adds aspect embeddings.
- **AT-LSTM** uses the aspect to compute attention over hidden states.
- **ATAE-LSTM** also appends the aspect vector to every word input.

Evaluation uses the SemEval 2014 benchmark (Pontiki et al., 2014), with restaurant and laptop reviews, under three-class and binary settings. The authors report state-of-the-art results over LSTM, TD-LSTM and TC-LSTM baselines. Attention visualizations show the model selecting aspect-relevant phrases. The work matters because it shows that conditioning attention on a learned aspect vector is a simple, parser-free way to separate opinions about different aspects within one sentence.

## Key points
- The data is the SemEval 2014 aspect-level dataset (Pontiki et al., 2014), with restaurant and laptop domains. The labels are positive, negative and neutral, and a binary setting drops the neutral instances.
- The aspect-aware attention computes word weights from the LSTM hidden states concatenated with a projected aspect embedding. The final sentence vector combines the attended representation r with the last hidden state h_N, a design borrowed from Rocktäschel et al., 2015.
- ATAE-LSTM injects the aspect embedding twice: once into each word input and once into the attention computation.
- According to the paper, TC-LSTM scored below both plain LSTM and TD-LSTM. The authors attribute this to target vectors built by averaging word embeddings, which do not fit well with the rest of the representation.
- Attention weights were visualized with the Heml tool. In the examples, the model picks out multi-word phrases such as "fastest delivery times" and multiple cues such as "tasteless" and "too sweet."

## Interesting ideas
- **Learned condition embeddings.** Representing each category (here, an aspect) as a trainable vector in its own space, instead of averaging word vectors, avoids a mismatch between word and category representations. This applies to any task conditioned on a small label set, such as topic-specific stance or entity-type-aware tagging.
- **Conditioning attention on a query vector.** Tiling a condition vector across all time steps and concatenating it with the hidden states before scoring is a cheap way to make attention query-dependent. It transfers to targeted extraction or question-focused classification.
- **Injecting the condition at input and at attention.** Adding the condition to both the inputs and the attention step lets every hidden state "know" the query, not just the scoring layer. This is a design choice worth testing whenever one encoder must serve several queries.
- **Residual mix of attention and last hidden state.** Combining the attended summary with the final hidden state gave better results in practice. It is a small, general trick for attention-pooled classifiers.
- **Parser-free modeling.** The authors argue that attention handles long, convoluted sentences where parse trees are unreliable. This makes the approach attractive for lower-resource languages where parsing errors are common.

*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 140884 whose bibliographies we read); 22 of them have a page here, 239 not yet.

- [Quantifying Attention Flow in Transformers](https://md.research.elamur.ai/free/en/papers/abnar-2020-quantifying-attention-flow-transformers/)
- [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://md.research.elamur.ai/free/en/papers/baly-2019-arsentd-lev-multi-topic-corpus/)
- [DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/baziotis-2017-datastories-semeval-2017-task-4/)
- [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/)
- [Recurrent Attention Network on Memory for Aspect Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/chen-2017-recurrent-attention-network-memory-aspect/)
- [Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring](https://md.research.elamur.ai/free/en/papers/dong-2017-attention-based-recurrent-convolutional-neural/)
- [A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples](https://md.research.elamur.ai/free/en/papers/findings-2021-wang-closer-look-into-robustness-neural/)
- [A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/jiang-2019-challenge-dataset-effective-models-aspect/)
- [Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification](https://md.research.elamur.ai/free/en/papers/lan-2017-multi-task-attention-based-neural/)
- [Transformation Networks for Target-Oriented Sentiment Classification](https://md.research.elamur.ai/free/en/papers/li-2018-transformation-networks-target-oriented-sentiment/)
- [A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict](https://md.research.elamur.ai/free/en/papers/liu-2022-dual-channel-framework-sarcasm-recognition/)
- [Guiding Computational Stance Detection with Expanded Stance Triangle Framework](https://md.research.elamur.ai/free/en/papers/liu-2023-guiding-computational-stance-detection-expanded/)
- [Arabic aspect based sentiment analysis using bidirectional GRU based models](https://md.research.elamur.ai/free/en/papers/m-2021-abdelgwad-arabic-aspect-based-sentiment-analysis/)
- [Arabic aspect sentiment polarity classification using BERT](https://md.research.elamur.ai/free/en/papers/m-2021-abdelgwad-arabic-aspect-sentiment-polarity-classification/)
- [The Risk of Racial Bias in Hate Speech Detection](https://md.research.elamur.ai/free/en/papers/sap-2019-risk-racial-bias-hate-speech/)
- [Is Attention Interpretable?](https://md.research.elamur.ai/free/en/papers/serrano-2019-is-attention-interpretable/)
- [Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree](https://md.research.elamur.ai/free/en/papers/sun-2019-aspect-level-sentiment-analysis-convolution/)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://md.research.elamur.ai/free/en/papers/sun-2019-utilizing-bert-aspect-based-sentiment/)
- [Relational Graph Attention Network for Aspect-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/wang-2020-relational-graph-attention-network-aspect/)
- [A Unified Generative Framework for Aspect-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/yan-2021-unified-generative-framework-aspect-based/)
- [Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks](https://md.research.elamur.ai/free/en/papers/zhang-2019-aspect-based-sentiment-classification-aspect/)
- [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://md.research.elamur.ai/free/en/papers/zhang-2021-aspect-sentiment-quad-prediction-as/)

## Cite

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

```bibtex
@inproceedings{wang2016attention,
  title = {{Attention-based LSTM for Aspect-level Sentiment Classification}},
  author = {Wang, Yequan and Huang, Minlie and Zhu, Xiaoyan and Zhao, Li},
  booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},
  year = {2016},
  doi = {10.18653/v1/d16-1058},
  url = {https://doi.org/10.18653/v1/d16-1058}
}
```

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