Sentiment analysis of text-to-speech input using latent affective mapping

To impart a congruent emotional quality to synthetic speech, it is expedient to leverage the overall polarity of the input text. This is feasible inasmuch as speech generation complies with the outcome of sentiment analysis. We have recently introduced latent affective mapping [1]-[3], a new approach to emotion detection which exploits two separate levels of semantic information: one that encapsulates the foundations of the domain considered, and one that specifically accounts for the overall affective fabric of the language. The ensuing framework exposes the emergent relationship between these two levels in order to advantageously inform affective evaluation. This paper applies latent affective mapping to the narrower problem of sentiment analysis, in order to achieve a more robust identification of the polarity of textual data. Empirical evidence gathered on the “Affective Text” portion of the SemEval-2007 corpus [4] shows that this approach is promising for automatic sentiment prediction in text. This bodes well as a first step in ensuring emotional congruence in text-to-speech synthesis.