  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.
