# Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks

arXiv: https://arxiv.org/abs/1804.06536 · HTML: https://mlsanity.com/paper/1804.06536/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 24 of 24)

- [How would Stance Detection Techniques Evolve after the Launch of ChatGPT?](https://mlsanity.com/paper/2212.14548/) (2022)
- [Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media](https://mlsanity.com/paper/2304.03087/) (2023)
- [On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training](https://mlsanity.com/paper/2304.09563/) (2023)
- [Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2110.13398/) (2021)
- [Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2203.16369/) (2022)
- [Exploiting Position Bias for Robust Aspect Sentiment Classification](https://mlsanity.com/paper/2105.14210/) (2021)
- [Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild](https://mlsanity.com/paper/2101.09449/) (2021)
- [BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification](https://mlsanity.com/paper/2110.00171/) (2021)
- [Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis](https://mlsanity.com/paper/2203.07090/) (2022)
- [PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2208.01368/) (2022)
- [Stance Detection on Social Media with Fine-Tuned Large Language Models](https://mlsanity.com/paper/2404.12171/) (2024)
- [Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews](https://mlsanity.com/paper/2109.07680/) (2021)
- [Arabic aspect based sentiment analysis using bidirectional GRU based models](https://mlsanity.com/paper/2101.10539/) (2021)
- [A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis](https://mlsanity.com/paper/2208.11283/) (2022)
- [Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2308.11447/) (2023)
- [Aspect Based Sentiment Analysis Using Spectral Temporal Graph Neural Network](https://mlsanity.com/paper/2202.06776/) (2022)
- [Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional Networks](https://mlsanity.com/paper/2204.12784/) (2022)
- [Aspect-specific Context Modeling for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2207.08099/) (2022)
- [Which Review Aspect Has a Greater Impact on the Duration of Open Peer Review in Multiple Rounds? -- Evidence from Nature Communications](https://mlsanity.com/paper/2606.21904/) (2026)
- [Position-based Contributive Embeddings for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2108.05098/) (2021)
- [Isomer: Transfer enhanced Dual-Channel Heterogeneous Dependency Attention Network for Aspect-based Sentiment Classification](https://mlsanity.com/paper/2112.03011/) (2021)
- [Dual-Attention Model for Aspect-Level Sentiment Classification](https://mlsanity.com/paper/2303.07689/) (2023)
- [PABSA: Hybrid Framework for Persian Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2510.04291/) (2025)
- [Aspect-Based Sentiment Evolution and its Correlation with Review Rounds in Multi-Round Peer Reviews: A Deep Learning Approach](https://mlsanity.com/paper/2606.24188/) (2026)

## Abstract (arXiv)

Aspect-level sentiment classification aims to identify the sentiment expressed towards some aspects given context sentences. In this paper, we introduce an attention-over-attention (AOA) neural network for aspect level sentiment classification. Our approach models aspects and sentences in a joint way and explicitly captures the interaction between aspects and context sentences. With the AOA module, our model jointly learns the representations for aspects and sentences, and automatically focuses on the important parts in sentences. Our experiments on laptop and restaurant datasets demonstrate our approach outperforms previous LSTM-based architectures.