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[PDF] Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis | Semantic Scholar
Source: semanticscholar.org
Article summary:
1. This paper presents a deep multi-task learning framework that jointly performs sentiment and emotion analysis.
2. The proposed framework uses multi-modal inputs (text, acoustic and visual frames) of a video to make decisions.
3. A context-level inter-modal attention framework is proposed for simultaneously extracting features from the different modalities.
Article analysis:
The article appears to be reliable and trustworthy as it provides detailed information about the proposed multi-task learning framework for multi-modal emotion recognition and sentiment analysis. The authors provide evidence for their claims by citing relevant research papers in the references section, which adds credibility to their work. Furthermore, the authors have provided clear explanations of their methodology, which makes it easier to understand the concept presented in the paper. However, there are some potential biases that should be noted when reading this article. For example, the authors may have overlooked certain counterarguments or unexplored points of consideration when presenting their findings. Additionally, there may be some promotional content present in the article as well as partiality towards certain ideas or concepts that could lead to one-sided reporting or unsupported claims. Finally, it is important to note that possible risks associated with using this technology are not discussed in detail in the paper, which could lead to an incomplete understanding of its implications.