Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis
Table 2: Datasets for training and test in our experiments.
['Dataset', 'Training #utterance', 'Training #video', 'Test #utterance', 'Test #video']
The CMU-MOSI dataset is rich in sentiment expression, consisting of 2,199 utterances, that is, 93 videos by 89 speakers. The videos involve a large array of topics such as movies, books, and other products. These videos were crawled from YouTube and segmented into utterances where each utterance is annotated with scores between −3 (strongly negative) and +3 (strongly positive) by five annotators. We take the average of these five annotations as the sentiment polarity and then consider only two classes, that is, “positive” and “negative”. Our training and test splits of the dataset are completely disjoint with respect to speakers. In order to better compare with the previous work, similar to [poria2018multimodal], we divide the dataset by 7:3 approximately, resulting in 1,616 and 583 utterances for training and test respectively.