Journal ArticleDOI
OntoSenticNet: A Commonsense Ontology for Sentiment Analysis
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OntoSenticNet is presented, a commonsense ontology for sentiment analysis based on SenticNet, a semantic network of 100,000 concepts based on conceptual primitives that has the capability of associating each concept with annotations contained in external resources.Abstract:
In this work, we present OntoSenticNet, a commonsense ontology for sentiment analysis based on SenticNet, a semantic network of 100,000 concepts based on conceptual primitives. The key characteristics of OntoSenticNet are: (i) the definition of precise conceptual hierarchy and properties associating concepts and sentiment values; (ii) the support for connecting external information (e.g., word embedding, domain information, and different polarity representations) to each individual defined within the ontology; and (iii) the capability of associating each concept with annotations contained in external resources (e.g., documents and multimodal resources).read more
Citations
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Journal ArticleDOI
Multimodal sentiment analysis using hierarchical fusion with context modeling
TL;DR: This article proposed a hierarchical feature fusion strategy that fuses the modalities two in two and only then fuses all three modalities in a hierarchical fashion to improve the multimodal fusion mechanism.
Journal ArticleDOI
Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks
TL;DR: Wang et al. as mentioned in this paper proposed a graph convolutional network based on SenticNet to leverage the affective dependencies of the sentence according to the specific aspect, called Sentic GCN.
Journal ArticleDOI
Learning multi-grained aspect target sequence for Chinese sentiment analysis
TL;DR: This paper formalizes the problem of aspect-level sentiment analysis from a different perspective, i.e., that sentiment at aspect target level should be the main focus and proposes to explicitly model the aspect target and conduct sentiment classification directly at the aspect targets level via three granularities.
Journal ArticleDOI
Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks
TL;DR: Wang et al. as mentioned in this paper proposed a graph convolutional network based on SenticNet to leverage the affective dependencies of the sentence according to the specific aspect, called Sentic GCN.
Journal ArticleDOI
A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks
TL;DR: This work proposes a novel hybrid framework for concept-level sentiment analysis in Persian language that integrates linguistic rules and deep learning to optimize polarity detection and outperforms state-of-the-art approaches.
References
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Proceedings Article
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Proceedings ArticleDOI
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Proceedings Article
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
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