Determining the emotional sentiment of a video remains a challenging task that requires multimodal, contextual understanding of a situation. In this paper, we describe our entry into the EmotiW 2020 Audio-Video Group Emotion Recognition Challenge to classify group videos containing large variations in language, people, and environment, into one of three sentiment classes. Our end-to-end approach consists of independently training models for different modalities, including full-frame video scenes, human body keypoints, embeddings extracted from audio clips, and image-caption word embeddings. Novel combinations of modalities, such as laughter and image-captioning, and transfer learning are further developed. We use fully-connected (FC) fusion ensembling to aggregate the modalities, achieving a best test accuracy of 63.9% which is 16 percentage points higher than that of the baseline ensemble.