The subliminal impact of framing of social, political and environmental issues such as climate change has been studied for long time in political science and communications research. Media framing offers interpretative package for average citizens on how to make sense of climate change and its consequences to their livelihoods, how to deal with its negative impacts, and which mitigation or adaptation policies to support. A line of related work has used bag of words and word-level features to detect frames automatically in text. Such works face limitations since standard keyword based features may not generalize well to accommodate surface variations in text when different keywords are used for similar concepts. In this paper, we develop a new type of textual features that generalize (subject, verb, object) triplets extracted from text, by clustering them into high-level concepts. We utilize these concepts as features to detect frames in text. Our corpus comprises more than 45,000 climate change related sentences. Expert coders annotated those sentences as frame/non-frame and framed sentences were mapped into one of four general frame categories: solution, problem threat, cause, and motivation. Compared to unigram and bigram based models, classification using our generalized concepts yielded better discriminating features and a higher accuracy classifier with a 12% boost (i.e. from 74% to 83% in f-measure) for frame/no frame detection.