# Left-Center-Right Separated Neural Network for Aspect-based Sentiment Analysis with Rotatory Attention

arXiv: https://arxiv.org/abs/1802.00892 · HTML: https://mlsanity.com/paper/1802.00892/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2018.

## Built on by (most cited first, 10 of 10)

- [A General Survey on Attention Mechanisms in Deep Learning](https://mlsanity.com/paper/2203.14263/) (2022)
- [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022)
- [Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification](https://mlsanity.com/paper/2103.11794/) (2021)
- [Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2103.15912/) (2021)
- [Deep Learning-based Sentiment Classification: A Comparative Survey](https://mlsanity.com/paper/2312.17253/) (2023)
- [Explaining a Neural Attention Model for Aspect-Based Sentiment Classification Using Diagnostic Classification](https://mlsanity.com/paper/2103.15927/) (2021)
- [Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2111.14988/) (2021)
- [Syntactic Fusion: Enhancing Aspect-Level Sentiment Analysis Through Multi-Tree Graph Integration](https://mlsanity.com/paper/2312.03738/) (2023)
- [Syntax-Informed Interactive Model for Comprehensive Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2312.03739/) (2023)
- [RotCAtt-TransUNet++: Novel Deep Neural Network for Sophisticated Cardiac Segmentation](https://mlsanity.com/paper/2409.05280/) (2024)

## Abstract (arXiv)

Deep learning techniques have achieved success in aspect-based sentiment analysis in recent years. However, there are two important issues that still remain to be further studied, i.e., 1) how to efficiently represent the target especially when the target contains multiple words; 2) how to utilize the interaction between target and left/right contexts to capture the most important words in them. In this paper, we propose an approach, called left-center-right separated neural network with rotatory attention (LCR-Rot), to better address the two problems. Our approach has two characteristics: 1) it has three separated LSTMs, i.e., left, center and right LSTMs, corresponding to three parts of a review (left context, target phrase and right context); 2) it has a rotatory attention mechanism which models the relation between target and left/right contexts. The target2context attention is used to capture the most indicative sentiment words in left/right contexts. Subsequently, the context2target attention is used to capture the most important word in the target. This leads to a two-side representation of the target: left-aware target and right-aware target. We compare our approach on three benchmark datasets with ten related methods proposed recently. The results show that our approach significantly outperforms the state-of-the-art techniques.