# LSA: Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation

arXiv: https://arxiv.org/abs/2110.08604 · HTML: https://mlsanity.com/paper/2110.08604/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Interactive Attention Networks for Aspect-Level Sentiment Classification](https://mlsanity.com/paper/1709.00893/) (2017)
- [Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks](https://mlsanity.com/paper/1909.03477/) (2019)
- [Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa](https://mlsanity.com/paper/2104.04986/) (2021)
- [Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks](https://mlsanity.com/paper/1909.02606/) (2019)
- [A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction](https://mlsanity.com/paper/1912.07976/) (2019)
- [Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification](https://mlsanity.com/paper/1906.04501/) (2019)
- [Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction](https://mlsanity.com/paper/2109.02403/) (2021)
- [Learning Explicit and Implicit Structures for Targeted Sentiment Analysis](https://mlsanity.com/paper/1909.07593/) (2019)

## Built on by (most cited first, 8 of 8)

- [InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2302.08624/) (2023)
- [A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2306.11260/) (2023)
- [Aspect-Based Sentiment Analysis Techniques: A Comparative Study](https://mlsanity.com/paper/2407.02834/) (2024)
- [Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews](https://mlsanity.com/paper/2408.13202/) (2024)
- [Domain-Adaptive Pre-Training for Arabic Aspect-Based Sentiment Analysis: A Comparative Study of Domain Adaptation and Fine-Tuning Strategies](https://mlsanity.com/paper/2509.16788/) (2025)
- [Large-Scale Aspect-Based Sentiment Analysis with Reasoning-Infused LLMs](https://mlsanity.com/paper/2601.03940/) (2026)
- [GHI: Graphormer over Conditioned Hypergraph Incidence for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2605.22228/) (2026)
- [Towards Defining, Measuring, and Leveraging Creativity in AI Applications](https://mlsanity.com/paper/2610.12845/) (2026)

## Abstract (arXiv)

Aspect sentiment coherency is an intriguing yet underexplored topic in the field of aspect-based sentiment classification. This concept reflects the common pattern where adjacent aspects often share similar sentiments. Despite its prevalence, current studies have not fully recognized the potential of modeling aspect sentiment coherency, including its implications in adversarial defense. To model aspect sentiment coherency, we propose a novel local sentiment aggregation (LSA) paradigm based on constructing a differential-weighted sentiment aggregation window. We have rigorously evaluated our model through experiments, and the results affirm the proficiency of LSA in terms of aspect coherency prediction and aspect sentiment classification. For instance, it outperforms existing models and achieves state-of-the-art sentiment classification performance across five public datasets. Furthermore, we demonstrate the promising ability of LSA in ABSC adversarial defense, thanks to its sentiment coherency modeling. To encourage further exploration and application of this concept, we have made our code publicly accessible. This will provide researchers with a valuable tool to delve into sentiment coherency modeling in future research.