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SenticNet: A Publicly Available Semantic Resource for Opinion Mining
TLDR
We developed SenticNet, a publicly available semantic resource for opinion mining built using common sense reasoning techniques together with an emotion categorization model and an ontology. Expand
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The Hourglass of Emotions
TLDR
We propose a novel biologically-inspired and psychologically-motivated emotion categorisation model that can potentially describe the full range of emotional experiences that are rooted in any of us. Expand
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Sentic Computing: Techniques, Tools, and Applications
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Group sparse regularization for deep neural networks
TLDR
In this paper, we address the challenging task of simultaneously optimizing (i) the weights of a neural network, (ii) the number of neurons for each hidden layer, and (iii) the subset of active input features (i.e., feature selection). Expand
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Agent-based computing from multi-agent systems to agent-based models: a visual survey
TLDR
Agent-based computing is a diverse research domain concerned with the building of intelligent software based on the concept of “agents”. Expand
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SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment Analysis
TLDR
We developed SenticNet 2, a publicly available semantic and affective resource for opinion mining and sentiment analysis for the development of affect-sensitive applications in fields such as social data mining. Expand
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Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining
TLDR
SenticNet 1.0 is one of the most widely used, publicly available resources for concept-based opinion mining. Expand
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Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up the Baselines
TLDR
In this paper, we explore three different deep-learning-based architectures for multimodal sentiment classification, each improving upon the previous. Expand
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Fusing audio, visual and textual clues for sentiment analysis from multimodal content
TLDR
We propose 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
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EmoSenticSpace: A novel framework for affective common-sense reasoning
TLDR
This paper proposes EmoSenticSpace, a new framework for affective common-sense reasoning that extends WordNet-Affect and SenticNet by providing both emotion labels and polarity scores for a large set of natural language concepts. Expand
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