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
Deep Mul Timodal Learning for Emotion Recognition in Spoken Language
Yue Gu,Shuhong Chen,Ivan Marsic +2 more
TL;DR: A novel deep multimodal framework to predict human emotions based on sentence-level spoken language by using a three-layer deep neural network to learn the correlations across modalities and train the feature extraction and fusion modules together, allowing optimal global fine-tuning of the entire structure.
Systematic Literature Review on Opinion Mining of Big Data for Government Intelligence.
Akshi Kumar,Abhilasha Sharma +1 more
TL;DR: This paper intends to provide a systematic literature review within the promising area of opinion mining and its application to the area of government.
Journal ArticleDOI
Recent advances in deep learning based sentiment analysis
TL;DR: A brief introduction to the recent advance of the deep learning-based methods in these sentiment analysis tasks, including summarizing the approaches and analyzing the dataset is given.
Proceedings ArticleDOI
Mutual Correlation Attentive Factors in Dyadic Fusion Networks for Speech Emotion Recognition
TL;DR: This work proposes an efficient dyadic fusion network that only relies on an attention mechanism to select representative vectors, fuse modality-specific features, and learn the sequence information, and significantly outperforms previous state-of-the-art research.
Book ChapterDOI
Benchmarking Multimodal Sentiment Analysis
TL;DR: In this article, a deep learning-based framework for multimodal sentiment analysis and emotion recognition is proposed, which leverages on the power of convolutional neural networks to obtain a performance improvement of 10% over the state-of-the-art by combining visual, text and audio features.
References
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