Technology has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. Much of the content being posted and consumed online is multimodal. With billions of phones, tablets and PCs shipping today with built-in cameras and a host of new video-equipped wearables like Google Glass on the horizon, the amount of video on the Internet will only continue to increase. It has become increasingly difficult for researchers to keep up with this deluge of multimodal content, let alone organize or make sense of it. Mining useful knowledge from video is a critical need that will grow exponentially, in pace with the global growth of content. This is particularly important in sentiment analysis, as both service and product reviews are gradually shifting from unimodal to multimodal. We present a novel method to extract features from visual and textual modalities using deep convolutional neural networks. By feeding such features to a multiple kernel learning classifier, we significantly outperform the state of the art of multimodal emotion recognition and sentiment analysis on different datasets.

The advent of the Social Web has provided netizens with new tools for creating and sharing, in a time- and cost-efficient way, their contents, ideas, and opinions with virtually the millions of people connected to the World Wide Web. This huge amount of information, however, is mainly unstructured as specifically produced for human consumption and, hence, it is not directly machine-processable. In order to enable a more efficient passage from unstructured information to structured data, aspect-based opinion mining models the relations between opinion targets contained in a document and the polarity values associated with these. Because aspects are often implicit, however, spotting them and calculating their respective polarity is an extremely difficult task, which is closer to natural language understanding rather than natural language processing. To this end, Sentic LDA exploits common-sense reasoning to shift LDA clustering from a syntactic to a semantic level. Rather than looking at word co-occurrence frequencies, Sentic LDA leverages on the semantics associated with words and multi-word expressions to improve clustering and, hence, outperform state-of-the-art techniques for aspect extraction.