  We utilize commonsense knowledge bases to address the problem of real- time
multimodal analysis. In particular, we focus on the problem of multimodal
sentiment analysis, which consists in the simultaneous analysis of different
modali- ties, e.g., speech and video, for emotion and polarity detection. Our
approach takes advantages of the massively parallel processing power of modern
GPUs to enhance the performance of feature extraction from different
modalities. In addition, in order to ex- tract important textual features from
multimodal sources we generate domain-specific graphs based on commonsense
knowledge and apply GPU-based graph traversal for fast feature detection. Then,
powerful ELM classifiers are applied to build the senti- ment analysis model
based on the extracted features. We conduct our experiments on the YouTube
dataset and achieve an accuracy of 78% which outperforms all previous systems.
In term of processing speed, our method shows improvements of several orders of
magnitude for feature extraction compared to CPU-based counterparts.
