AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis
This work addresses the need for scalable sentiment analysis in domains like social media and business by enabling multi-label predictions without costly labeled data, though it is incremental as it builds on prior weak supervision methods.
The paper tackles the problem of multi-label aspect-based sentiment analysis without labeled data by proposing an extremely weakly supervised framework that uses only a single word per class as initial information, and it outperforms other weakly supervised baselines on four benchmark datasets.
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.