Journal ArticleDOI
YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context
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TLDR
Experimental results indicate that training on written movie reviews is a promising alternative to exclusively using (spoken) in-domain data for building a system that analyzes spoken movie review videos, and that language-independent audio-visual analysis can compete with linguistic analysis.Abstract:
This work focuses on automatically analyzing a speaker's sentiment in online videos containing movie reviews. In addition to textual information, this approach considers adding audio features as typically used in speech-based emotion recognition as well as video features encoding valuable valence information conveyed by the speaker. Experimental results indicate that training on written movie reviews is a promising alternative to exclusively using (spoken) in-domain data for building a system that analyzes spoken movie review videos, and that language-independent audio-visual analysis can compete with linguistic analysis.read more
Citations
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Proceedings ArticleDOI
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
TL;DR: The Multimodal EmotionLines Dataset (MELD) as discussed by the authors is a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue.
Journal ArticleDOI
A Review and Meta-Analysis of Multimodal Affect Detection Systems
TL;DR: A quantitative review and meta-analysis of 90 Multimodal affect detection systems revealed that MM systems were consistently (85% of systems) more accurate than their best unimodal counterparts, with an average improvement of 9.83% (median of 6.60%).
Journal ArticleDOI
A survey of multimodal sentiment analysis
Mohammad Soleymani,David Garcia,Brendan Jou,Björn Schuller,Björn Schuller,Björn Schuller,Shih-Fu Chang,Maja Pantic,Maja Pantic +8 more
TL;DR: The thesis is that multimodal sentiment analysis holds a significant untapped potential with the arrival of complementary data streams for improving and going beyond text-based sentiment analysis.
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Memory Fusion Network for Multi-view Sequential Learning
Amir Zadeh,Paul Pu Liang,Navonil Mazumder,Soujanya Poria,Erik Cambria,Louis-Philippe Morency +5 more
TL;DR: Memory Fusion Network (MFN) as discussed by the authors explicitly accounts for both interactions in a neural architecture and continuously models them through time by using a memory fusion network to learn view-specific interactions and cross-view interactions.
Journal ArticleDOI
Sentic patterns: dependency-based rules for concept-level sentiment analysis
TL;DR: The authors proposed a concept-level sentiment analysis that merges linguistics, common-sense computing, and machine learning for improving the accuracy of tasks such as polarity detection, by allowing sentiments to flow from concept to concept based on the dependency relation of the input sentence, in particular, achieving a better understanding of the contextual role of each concept within the sentence and, hence, obtaining a polarity detector that outperforms state-of-the-art statistical methods.
References
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