Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis
Table 1: This table presents accuracy and F-score for One-Step DCCA and baseline methods on MOSI, MOSEI and Debate Emotion data sets. Best performing algorithm and modality are indicated in boldface. Star marked results correspond to numbers reported in original publication.
['Data View', 'Debate Emotion Acc', 'Debate Emotion F-score', 'CMU-MOSI Acc', 'CMU-MOSI F-score', 'CMU-MOSEI Acc', 'CMU-MOSEI F-score']
Furthermore, learning combinations in a correlated space using DCCA consistently outperforms other baselines on all three data sets. Note that, the performances reported for the bc-LSTM+3D-CNN+openSMILE baseline and the Graph Memory Fusion Network Besides, these two baselines do not directly compare against our method, since they operate at the word level with different text features. However, since these algorithms achieve the state-of-the-art on the MOSEI and MOSI data set, they are included in this work. All other baselines were reproduced on the test data sets using parameters reported in their original implementations.