In the fields of sentiment and emotion recognition, bag of words modeling has lately become popular for the estimation of valence in text. A typical application is the evaluation of reviews of e.g. movies, music, or games. In this respect we suggest the use of back-off N-Grams as basis for a vector space construction in order to combine advantages of word-order modeling and easy integration into potential acoustic feature vectors intended for spoken document retrieval. For a fine granular estimate we consider data-driven regression next to classification based on support vector machines. Alternatively the on-line knowledge sources ConceptNet, general inquirer, and WordNet not only serve to reduce out-of-vocabulary events, but also as basis for a purely linguistic analysis. As special benefit, this approach does not demand labeled training data. A large set of 100 k movie reviews of 20 years stemming from Metacritic is utilized throughout extensive parameter discussion and comparative evaluation effectively demonstrating efficiency of the proposed methods.