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An essential step in the generation of expressive speech synthesis is the automatic detection and classification of emotions most likely to be present in textual input. Though increasingly data-driven, emotion analysis still relies on critical expert knowledge in order to isolate the emotional keywords or keysets necessary to the construction of affective categories. This makes it vulnerable to any discrepancy between the ensuing taxonomy of affective states and the underlying domain of discourse. This paper proposes a more general framework, latent affective mapping, which exploits two separate levels of semantic information: the first one encapsulates the foundations of the domain considered, while the second one specifically accounts for the overall affective fabric of the language. Exposing the emergent relationship between these two levels advantageously steers the emotion classification process. Empirical evidence suggests that this approach is effective for automatic emotion analysis in text. This bodes well for its deployability toward naturally expressive speech synthesis.