# Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis

arXiv: https://arxiv.org/abs/2402.13722 · HTML: https://mlsanity.com/paper/2402.13722/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [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)
- [Adaptive Attention Span in Transformers](https://mlsanity.com/paper/1905.07799/) (2019)
- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2107.12214/) (2021)
- [An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/1906.06906/) (2019)
- [Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extractionwith Rich Syntactic Knowledge](https://mlsanity.com/paper/2105.02520/) (2021)
- [A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2204.12674/) (2022)
- [AMOM: Adaptive Masking over Masking for Conditional Masked Language Model](https://mlsanity.com/paper/2303.07457/) (2023)
- [Aspect-based Sentiment Analysis through EDU-level Attentions](https://mlsanity.com/paper/2202.02535/) (2022)

## Built on by (most cited first, 1 of 1)

- [Beyond Cosine Similarity: Zero-Initialized Residual Complex Projection for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2603.28205/) (2026)

## Abstract (arXiv)

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained linguistics problem that entails the extraction of multifaceted aspects, opinions, and sentiments from the given text. Both standalone and compound ABSA tasks have been extensively used in the literature to examine the nuanced information present in online reviews and social media posts. Current ABSA methods often rely on static hyperparameters for attention-masking mechanisms, which can struggle with context adaptation and may overlook the unique relevance of words in varied situations. This leads to challenges in accurately analyzing complex sentences containing multiple aspects with differing sentiments. In this work, we present adaptive masking methods that remove irrelevant tokens based on context to assist in Aspect Term Extraction and Aspect Sentiment Classification subtasks of ABSA. We show with our experiments that the proposed methods outperform the baseline methods in terms of accuracy and F1 scores on four benchmark online review datasets. Further, we show that the proposed methods can be extended with multiple adaptations and demonstrate a qualitative analysis of the proposed approach using sample text for aspect term extraction.