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One of the aims of aspect-oriented requirements engineering is to address the composability and subsequent analysis of crosscutting and non-crosscutting concerns during requirements engineering. Composing concerns may help to reveal conflicting dependencies that need to be identified and resolved. However, detecting conflicts in a large set of textual aspect-oriented requirements is an error-prone and time-consuming task. This paper presents EA-analyzer, the first automated tool for identifying conflicts in aspect-oriented requirements specified in natural-language text. The tool is based on a novel application of a Bayesian learning method that has been effective at classifying text. We present an empirical evaluation of the tool with three industrial-strength requirements documents from different real-life domains. We show that the tool achieves up to 92.97% accuracy when one of the case study documents is used as a training set and the other two as a validation set.