Attention-based Multi-modal Sentiment Analysis and Emotion Detection in Conversation using RNN

Author
Keywords
Abstract
The availability of an enormous quantity of multimodal data and its widespread applications, automatic sentiment analysis and emotion classification in the conversation has become an interesting research topic among the research community. The interlocutor state, context state between the neighboring utterances and multimodal fusion play an important role in multimodal sentiment analysis and emotion detection in conversation. In this article, the recurrent neural network (RNN) based method is developed to capture the interlocutor state and contextual state between the utterances. The pair-wise attention mechanism is used to understand the relationship between the modalities and their importance before fusion. First, two-two combinations of modalities are fused at a time and finally, all the modalities are fused to form the trimodal representation feature vector. The experiments are conducted on three standard datasets such as IEMOCAP, CMU-MOSEI, and CMU-MOSI. The proposed model is evaluated using two metrics such as accuracy and F1-Score and the results demonstrate that the proposed model performs better than the standard baselines.
Year of Publication
9998
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
In Press
Start Page
1
Issue
In Press
Number
In Press
Number of Pages
10
Date Published
07/2020
ISSN Number
1989-1660
URL
https://www.ijimai.org/journal/sites/default/files/2020-08/ip2020_07_004_0.pdf
DOI
10.9781/ijimai.2020.07.004
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