Aspect-based sentiment analysis (ABSA) is a natural language processing (NLP) task, ascribing precise sentiment linkages to specific entities and issues in text data. This paper addresses critical shortcomings in current ABSA methods, particularly the issues of limited aspects, training set biases, and lack of comprehensive stance-coded datasets. First, we develop a scalable MaskedABSA approach that masks aspect terms in training sentences to enable unbiased sentiment inference from the context alone. We show that the proposed method surpasses the state-of-the-art solutions in accuracy for the aspect term sentiment classification task, as verified by the SemEval datasets. Furthermore, we tackle the perennial challenges of limited training resources and the prohibitive costs of manual annotation in ABSA dataset creation by introducing an innovative weak supervision technique capitalizing on the inherent community clustering properties found within social media datasets. We utilize community detection algorithms to partition a share network into polarized groups with homogeneous adversarial stances, allowing large-scale aspect-based sentiment analysis dataset curation without labor intensive manual labeling. Our methodology is also validated using a real-world polarized dataset comprising diverse aspects and stances to showcase its efficacy and scalability.