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A review of affective computing: From unimodal analysis to multimodal fusion
TLDR
This first of its kind, comprehensive literature review of the diverse field of affective computing focuses mainly on the use of audio, visual and text information for multimodal affect analysis, and outlines existing methods for fusing information from different modalities. Expand
SenticNet: A Publicly Available Semantic Resource for Opinion Mining
TLDR
SenticNet is a publicly available resource for opinion mining built exploiting AI and Semantic Web techniques and uses dimensionality reduction to infer the polarity of common sense concepts and hence provide a public resource for mining opinions from natural language text at a semantic, rather than just syntactic, level. Expand
The Hourglass of Emotions
TLDR
A novel biologically-inspired and psychologically-motivated emotion categorisation model that represents affective states both through labels and through four independent but concomitant affective dimensions, which can potentially describe the full range of emotional experiences that are rooted in any of us. Expand
Group sparse regularization for deep neural networks
TLDR
The group Lasso penalty is extended, originally proposed in the linear regression literature, to impose group-level sparsity on the networks connections, where each group is defined as the set of outgoing weights from a unit. Expand
SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment Analysis
TLDR
By providing the semantics and sentics associated with over 14,000 concepts, SenticNet 2 represents one of the most comprehensive semantic resources for the development of affect-sensitive applications in fields such as social data mining, multimodal affective HCI, and social media marketing. Expand
Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis
TLDR
A novel method to extract features from visual and textual modalities using deep convolutional neural networks and significantly outperform the state of the art of multimodal emotion recognition and sentiment analysis on different datasets is presented. Expand
Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining
TLDR
The presented methodology enriches SenticNet concepts with affective information by assigning an emotion label by way of concept-based opinion mining. Expand
Agent-based computing from multi-agent systems to agent-based models: a visual survey
TLDR
This paper uses Scientometric analysis to analyze all sub-domains of agent-based computing, and results include the identification of the largest cluster based on keywords, the timeline of publication of index terms, the core journals and key subject categories. Expand
Fusing audio, visual and textual clues for sentiment analysis from multimodal content
TLDR
This paper proposes a novel methodology for multimodal sentiment analysis, which consists in harvesting sentiments from Web videos by demonstrating a model that uses audio, visual and textual modalities as sources of information. Expand
Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis
TLDR
A knowledge-rich solution to targeted aspect-based sentiment analysis with a specific focus on leveraging commonsense knowledge in the deep neural sequential model is proposed and shown to outperform state-of-the-art methods in two targeted aspect sentiment tasks. Expand
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