  People are sharing their opinions, stories and reviews through online video
sharing websites every day. Studying sentiment and subjectivity in these
opinion videos is experiencing a growing attention from academia and industry.
While sentiment analysis has been successful for text, it is an understudied
research question for videos and multimedia content. The biggest setbacks for
studies in this direction are lack of a proper dataset, methodology, baselines
and statistical analysis of how information from different modality sources
relate to each other. This paper introduces to the scientific community the
first opinion-level annotated corpus of sentiment and subjectivity analysis in
online videos called Multimodal Opinion-level Sentiment Intensity dataset
(MOSI). The dataset is rigorously annotated with labels for subjectivity,
sentiment intensity, per-frame and per-opinion annotated visual features, and
per-milliseconds annotated audio features. Furthermore, we present baselines
for future studies in this direction as well as a new multimodal fusion
approach that jointly models spoken words and visual gestures.
