# Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans

arXiv: https://arxiv.org/abs/2010.02696 · HTML: https://mlsanity.com/paper/2010.02696/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional Networks](https://mlsanity.com/paper/2204.12784/) (2022)
- [UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models](https://mlsanity.com/paper/2104.04770/) (2021)
- [Explicit Interaction Network for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2106.11148/) (2021)

## Abstract (arXiv)

Aspect based sentiment analysis, predicting sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification. However, these works are either not able to capture opinion spans as a whole, or not able to capture variable-length opinion spans. In this paper, we present a neat and effective structured attention model by aggregating multiple linear-chain CRFs. Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features. The experimental results on four datasets demonstrate the effectiveness of the proposed model, and our analysis demonstrates that our model can capture aspect-specific opinion spans.