Fuzzy Explainable Attention-based Deep Active Learning on Mental-Health Data

In this paper, we propose a fuzzy classification deep attention-based model that expands emotional lexicons by using linguistic properties of actual patient authored texts. The active learning methods can expand the trained dataset and fuzzy rules over some time. As a result, the model itself can reduce its labeling efforts for mental health application. Thus, the designed model can solve issues related to vocabulary sizes per class, data sources, methods of creation, and create a baseline for human performance levels. This paper also gives fuzzy explainability by visualizing weighted words. Our proposed method uses a similarity-based method that includes a subset of unstructured data as the training set. Next, using an active learning mechanism cycle, our method updates the training model using new training points. This cycle is repeatedly performed until an optimal solution is reached. The designed model also converts all unlabeled texts into the training set. Our in-depth experimental results show that the emotion-based expansion enhances the testing accuracy and helps to build quality rules.