Unimodal and multimodal uni-task, bi-task, and tri-task learning models for sentiment analysis using the CMU-MOSI database.
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In uni-task models we perform regression experiments to predict sentiment scores.
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In bi-task models we perform multi-task learning experiments which have sentiment score regression as the main task, and intensity or polarity classification as the auxiliary task.
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In tri-task models we perform multi-task learing experiments which have sentiment score regression as the main task, and intensity and polarity classification as the auxiliary tasks.
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In multimodal models we compare Early Fusion, Late Fusion, Hierarchical Fusion, and Tensor Fusion Network.
These codes are for our ACL2018 Computational Modeling of Human Multimodal Language Workshop paper:
@inproceedings{tian2018polarity,
title={Polarity and Intensity: the Two Aspects of Sentiment Analysis},
author={Tian, Leimin and Lai, Catherine and Moore, Johanna},
booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics},
pages={to appear},
year={2018}
}"acl2018_appendix.pdf" contains results on the training and validation sets of the CMU-MOSI database, as an appendix to the test results reported in the paper.
The Hierarchical Fusion model in the multimodal experiments is based on our previous work:
@inproceedings{tian2016recognizing,
title={Recognizing emotions in spoken dialogue with hierarchically fused acoustic and lexical features},
author={Tian, Leimin and Moore, Johanna and Lai, Catherine},
booktitle={Spoken Language Technology Workshop (SLT), 2016 IEEE},
pages={565--572},
year={2016},
organization={IEEE}
}