Affective computing researchers have recently been focusing on continuous emotion dimensions like arousal and valence. This dual coordinate affect space can explain many of the discrete emotions like sadness, anger, joy, etc. In the area of continuous emotion recognition, Principal Component Analysis (PCA) models are generally used to enhance the performance of various image and audio features by projecting them to a new space where the new features are less correlated. We instead, propose that quantizing and projecting the features to a latent topic space performs better than PCA. Specifically we extract these topic features using Latent Dirichlet Allocation (LDA) models. We show that topic models project the original features to a latent feature space that is more coherent and useful for continuous emotion recognition than PCA. Unlike PCA where no semantics can be attributed to the new features, topic features can have a visual and semantic interpretation which can be used in personalized HCI applications and Assistive technologies. Our hypothesis in this work has been validated using the AVEC 2012 continuous emotion challenge dataset.