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In the last decade, video blogs (vlogs) have become an extremely popularmethod through which people express sentiment. The ubiquitousness of thesevideos has increased the importance of multimodal fusion models, whichincorporate video and audio features with traditional text features forautomatic sentiment detection. Multimodal fusion offers a unique opportunity tobuild models that learn from the full depth of expression available to humanviewers. In the detection of sentiment in these videos, acoustic and videofeatures provide clarity to otherwise ambiguous transcripts. In this paper, wepresent a multimodal fusion model that exclusively uses high-level video andaudio features to analyze spoken sentences for sentiment. We discardtraditional transcription features in order to minimize human intervention andto maximize the deployability of our model on at-scale real-world data. Weselect high-level features for our model that have been successful in nonaffectdomains in order to test their generalizability in the sentiment detectiondomain. We train and test our model on the newly released CMU MultimodalOpinion Sentiment and Emotion Intensity (CMUMOSEI) dataset, obtaining an F1score of 0.8049 on the validation set and an F1 score of 0.6325 on the held-outchallenge test set.

Getting the subtext without the text: Scalable multimodal sentiment classification from visual and acoustic modalities