---
title: "Multi-grained Attention Network for Aspect-Level Sentiment"
description: "Earlier attention models build their attention guides by averaging the aspect or… 0.7038 accuracy. 22 references, cited by 53 papers here."
authors: ["Feifan Fan", "Yansong Feng", "Dongyan Zhao"]
year: 2018
venue: "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D18-1380/"
doi: "10.18653/v1/d18-1380"
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/fan-2018-multi-grained-attention-network-aspect/"
---
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# Multi-grained Attention Network for Aspect-Level Sentiment Classification

**Authors:** Feifan Fan, [Yansong Feng](https://md.research.elamur.ai/free/en/authors/yansong-feng/) and [Dongyan Zhao](https://md.research.elamur.ai/free/en/authors/dongyan-zhao/) · **Year:** 2018 · **Venue:** [Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2018](https://md.research.elamur.ai/free/en/venues/emnlp/2018/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D18-1380/) · [PDF](https://aclanthology.org/D18-1380.pdf) · [DOI 10.18653/v1/d18-1380](https://doi.org/10.18653/v1/d18-1380) · [OpenAlex](https://openalex.org/W2891778157)

**Citations:** 520 citations (OpenAlex, 2026-10-14) · [53](https://md.research.elamur.ai/free/en/papers/fan-2018-multi-grained-attention-network-aspect/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper addresses aspect-level sentiment classification, which means predicting whether a sentence expresses positive, neutral or negative sentiment toward a specific aspect term. Earlier attention models build their attention guides by averaging the aspect or context vectors. The authors argue that this "coarse" pooling discards information when aspects span several words or contexts are long. They propose MGAN, which runs a BiLSTM over GloVe embeddings and adds position weighting so that words near the aspect count more. MGAN combines two kinds of attention. Coarse-grained bidirectional attention (C-Aspect2Context, C-Context2Aspect) works from pooled vectors. Fine-grained attention (F-Aspect2Context, F-Context2Aspect) uses a word-by-word alignment matrix. MGAN also adds an aspect alignment loss, which pushes apart the attention distributions of aspects that share a sentence but differ in polarity. The authors present this as the first use of such inter-aspect interactions. On SemEval 2014 laptop and restaurant reviews and a Twitter set, MGAN beats strong baselines such as RAM, MemNet and IAN. This shows that word-level interactions and joint modelling of co-occurring aspects carry useful signal.

## Key points
- **Main results:** MGAN reports the following accuracy / Macro-F1 scores:
  - Laptop: 0.7539 / 0.7247 (best baseline RAM: 0.7449 / 0.7135).
  - Restaurant: 0.8125 / 0.7194.
  - Twitter: 0.7254 / 0.7081 (best baseline BILSTM-ATT-G: 0.7038 accuracy).
- **Ablation on laptop:** accuracy rises from MGAN-C (0.7273) to MGAN-F (0.7398), then MGAN-CF (0.7445), then full MGAN (0.7539). The fine-grained attention helps most on laptop, which has the largest share of aspects longer than two words (9.24%).
- **Alignment loss and Twitter:** the alignment loss adds nothing on Twitter. Nearly all tweets (99.91%) contain a single aspect, so MGAN there is equivalent to MGAN-CF.
- **Multi-aspect sentences:** these are common in the review data. About 36% of laptop sentences and 49% of restaurant sentences have two or more aspects, which is where the alignment loss pays off.
- **Case study:** attention visualisation on a sentence about "resolution" and "fonts" shows that the alignment loss widens the attention gaps on polarity-bearing words such as "small" and "higher".

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

## Institutions

- [Peking University](https://md.research.elamur.ai/free/en/universities/peking-university/) — University, Beijing, China · 100 works · 35,618 citations
- [Beijing Institute of Big Data Research](https://md.research.elamur.ai/free/en/labs/beijing-institute-of-big-data-research/) — Research institute, Beijing, China · 2 works · 839 citations

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🧭 Topics (1): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 608 works

## Cite

Cite as: Feifan Fan, Yansong Feng and Dongyan Zhao. 2018. Multi-grained Attention Network for Aspect-Level Sentiment Classification. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.

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

```bibtex
@inproceedings{fan2018multi,
  title = {{Multi-grained Attention Network for Aspect-Level Sentiment Classification}},
  author = {Feifan Fan, Yansong Feng and Dongyan Zhao},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1380},
  url = {https://doi.org/10.18653/v1/d18-1380}
}
```

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## References

22 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*