AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis
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Abstract (arXiv)
Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 27 of its 63 references that are in the map. First posted on arXiv 7 Nov 2022.
Builds on
27 of 63 references.
References · 27
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15 more
- 2021Learning Span-Level Interactions for Aspect Sentiment Triplet Extractioncited by 60
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- 2020Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generationcited by 27
- 2019Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervisioncited by 25
- 2021Solving Aspect Category Sentiment Analysis as a Text Generation Taskcited by 21
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- 2021Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classificationcited by 13
- 2019A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysiscited by 8
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2 papers.