  Most recent works on sentiment analysis have exploited the text modality.
However, millions of hours of video recordings posted on social media platforms
everyday hold vital unstructured information that can be exploited to more
effectively gauge public perception. Multimodal sentiment analysis offers an
innovative solution to computationally understand and harvest sentiments from
videos by contextually exploiting audio, visual and textual cues. In this
paper, we, firstly, present a first of its kind Persian multimodal dataset
comprising more than 800 utterances, as a benchmark resource for researchers to
evaluate multimodal sentiment analysis approaches in Persian language.
Secondly, we present a novel context-aware multimodal sentiment analysis
framework, that simultaneously exploits acoustic, visual and textual cues to
more accurately determine the expressed sentiment. We employ both
decision-level (late) and feature-level (early) fusion methods to integrate
affective cross-modal information. Experimental results demonstrate that the
contextual integration of multimodal features such as textual, acoustic and
visual features deliver better performance (91.39%) compared to unimodal
features (89.24%).
