Hormonal therapy adherence is challenging for many patients with hormone-receptor-positive breast cancer. Gaining intuition into their adherence behavior would assist in improving outcomes by pinpointing, and eventually addressing, why patients fail to adhere. While traditional adherence studies rely on survey-based methods or electronic medical records, online health communities provide a supplemental data source to learn about such behavior and often on a much larger scale. In this paper, we focus on an online breast cancer discussion forum and propose a framework to automatically extract hormonal therapy adherence behavior (HTAB) mentions. The framework compares medical term usage when describing when a patient is taking hormonal therapy medication and interrupting their treatment (e.g., stop/pause taking medication). We show that by using shallow neural networks, in the form of wordlvec, the learned features can be applied to build efficient HTAB mention classifiers. Through medical term comparison, we find that patients who exhibit an interruption behavior are more likely to mention depression and their care providers, while patients with continuation behavior are more likely to mention common side effects (e.g., hot flashes, nausea and osteoporosis), vitamins and exercise.