One of the main advantages of analyzing videos over texts alone, areis the presence of rich [[sentiment]] cues in visual data.<ref>{{cite journal |last1=Poria |first1=Soujanya |last2=Cambria |first2=Erik |last3=Hazarika |first3=Devamanyu |last4=Majumder |first4=Navonil |last5=Zadeh |first5=Amir |last6=Morency |first6=Louis-Philippe |title=Context-Dependent Sentiment Analysis in User-Generated Videos |journal=Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) |date=2017 |doi=10.18653/v1/p17-1081 |url=https://doi.org/10.18653/v1/P17-1081 |publisher=Association for Computational Linguistics}}</ref> Visual features include [[facial expression]]s, which are principal signs of understanding [[sentiment]]s and [[emotion]]s, as they are a main channel of forming a person's present state of mind.<ref name="s1"></ref> Specifically, [[smile]], is considered to be one of the most predictive visual cues in multimodal sentiment analysis.<ref name="s2"></ref> OpenFace is an open-source facial analysis toolkit available for extracting and understanding such visual features.<ref>{{cite journal |title=OpenFace: An open source facial behavior analysis toolkit - IEEE Conference Publication |journal=ieeexplore.ieee.org |url=https://ieeexplore.ieee.org/document/7477553/}}</ref>
