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Hate speech in online comments is a serious challenge for media actors, platforms, legal authorities, and the general public alike. This form of communication can impair the quality of online discussions, as well as poisoning public discourse and civic life. This explains the plethora of studies from different disciplines addressing the topic. However, the diversity of disciplines and approaches complicate a common understanding of hate speech. Frequently, definitions are too broad, as they include factors such as the intention of the commenter or the consequences for stigmatized groups. Additionally, binary categorizations (hate/no hate) applied in some disciplines fail to consider that hate speech varies in intensity, while ignoring the qualitative differences in its dimensions. The present research suggests a feature-based approach to analyzing hate speech in online comments, focusing on manifest features (group-related labels, swear words, accusations of certain traits and actions, and treatment recommendations). Use of the approach is demonstrated in a pilot study.