Submitted to Special Issue on “Computational Approaches to Analysis of Emotion in Text”
Guest editors: Diana Inkpen and Carlo Strapparava
Initial publication: NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, June 5, 2010, Los Angeles, CA
Though data-driven in nature, emotion analysis based on latent semantic analysis still relies on some measure of expert knowledge 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 strategy which leverages two separate semantic levels: one that encapsulates the foundations of the domain considered, and one that specifically accounts for the overall affective fabric of the language. Exposing the emergent relationship between these two levels advantageously informs the emotion classification process. Empirical evidence suggests that this approach is promising for automatic emotion analysis in text. This bodes well for its deployability in a variety of applications, such as sentiment prediction.