A Mood-based Genre Classification of Television Content \titlenoteThis work was supported by Science Foundation Ireland through the CLARITY Centre for Sensor Web Technologies under grant number 07/CE/I1147 and through the Insight Centre for Data Analytics under grant SFI/12/RC/2289.
Table 3: Genre classification performance.
['[BOLD] Genre', '[BOLD] VSM  [BOLD] TP', '[BOLD] VSM  [BOLD] FP', '[BOLD] VSM  [BOLD] AUC', '[BOLD] Meta-features  [BOLD] TP', '[BOLD] Meta-features  [BOLD] FP', '[BOLD] Meta-features  [BOLD] AUC']
In general, VSM provided better accuracy compared to the meta-features approach. The exception to this trend was the horror genre, where the meta-features classifier obtained an AUC score of 0.865 compared to 0.649 for VSM. For both approaches, the best classification accuracy was achieved in respect of the newscast genre, with very high AUC scores (>0.9) observed in both cases. This finding indicates that a relatively distinct vocabulary is associated with this genre in particular.