Abstract
Due to the ever-expanding volumes of information available on social media, the need for reliable and efficient automated text understanding mechanisms becomes evident. Unfortunately, most current approaches rely on black-box solutions rooted in deep learning technologies. In order to provide a more transparent and interpretable framework for extracting intrinsic text characteristics like emotions, hate speech and irony, we propose to integrate fuzzy rough set techniques and text embeddings. We apply our methods to different classification problems originating from Semantic Evaluation (SemEval) competitions, and demonstrate that their accuracy is on par with leading deep learning solutions.
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