Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines
TABLE I: Person-Independent Train/Test split details of each dataset (≈ 70/30 % split). Note: X→Y represents train: X and test: Y; Validation sets are extracted from the shuffled train sets using 80/20 % train/val ratio.
['Dataset', 'Train  [ITALIC] utterance', 'Train  [ITALIC] video', 'Test  [ITALIC] utterance', 'Test  [ITALIC] video']
They collected 80 product review and recommendation videos from YouTube. Each video was segmented into its utterances (498 in total) and each of these was categorized by a sentiment label (positive, negative and neutral). On average, each video has 6 utterances and each utterance is 5 seconds long. In our experiment, we did not consider neutral labels, which led to the final dataset consisting of 448 utterances. We dropped the neutral label to maintain consistency with previous work. In a similar fashion, Zadeh et al. The videos address a large array of topics, such as movies, books, and products. In the experiment to address the generalizability issues, we trained a model on MOSI and tested on MOUD.