Draft:SenticNet
Submission declined on 19 July 2026 by CopyleftEverything (talk).
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Submission declined on 11 July 2026 by ChrysGalley (talk). This draft appears to contain text generated by a large language model (such as ChatGPT). You cannot use LLMs to generate article content.
Declined by ChrysGalley 25 days ago.LLM-generated pages with certain obvious signs of being machine generated may be deleted without notice. Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject. See the advice page on large language models for more information. | |
Comment: Likely LLM authorship. WP:AIBOLD, WP:AILIST, WP:AIPUFFERY. Rewrite article with human hands and verify all claims. CopyleftEverything (talk) 01:55, 19 July 2026 (UTC)
- Comment: We can't accept AI submissions. ChrysGalley (talk) 10:01, 11 July 2026 (UTC)
- Comment: In accordance with Wikipedia's Conflict of interest guideline, I disclose that I have a conflict of interest regarding the subject of this article. ~2026-38003-29 (talk) 09:49, 11 July 2026 (UTC)
SenticNet is an open-source knowledge base and neurosymbolic artificial intelligence framework used for concept-level sentiment analysis and natural language understanding. It is developed to support text analysis by shifting from word-frequency statistics to a representation model that matches the semantic and emotional meaning of multi-word expressions.[1]
The project is developed by a research group at the College of Computing and Data Science (CCDS) within Nanyang Technological University (NTU) in Singapore, along with external open-source contributors.[2]
History
[edit]SenticNet began in 2009 at the MIT Media Lab as part of a Cooperative Awards in Science and Engineering (CASE) project involving the MIT Media Lab, the University of Stirling, and Sitekit Solutions Ltd.[2] The resource has since undergone multiple updates to support applications in social data analytics, human–computer interaction, financial modeling, and healthcare.
Architecture and methodology
[edit]SenticNet is built automatically via graph-mining, multidimensional scaling, and representation learning techniques rather than relying on manual annotation. It extracts affective and commonsense data from foundational resources such as WordNet-Affect, Open Mind Common Sense (OMCS), and GECKA.[3]
Knowledge representation
[edit]Based on Marvin Minsky's panalogy principle—which suggests representing data in parallel formats to increase structural robustness—SenticNet organizes its knowledge base across three primary layouts:
- Semantic Network: A directed graph that maps contextual associations and relational links between concepts.
- Matrix Representation: Adjacency matrices that track the topological layout of the graph.
- Vector Space (AffectiveSpace): A high-dimensional vector space where multi-word concepts are positioned based on semantic similarity to allow geometric calculation and reasoning.[4]
Sentiment inference mechanisms
[edit]The extraction of semantic meaning and emotional metrics relies on several core components:
- Spreading Activation: An algorithm that passes emotional valences along the nodes of the semantic graph to assign meaning to unlabelled multi-word concepts.
- Sentic Neurons: Deep learning structures designed to model linguistic dependencies.
- The Hourglass of Emotions: An emotion categorization model that maps emotional variables into four independent dimensions: Pleasantness, Attention, Sensitivity, and Aptitude.[3]
Version history
[edit]SenticNet has grown through sequential public releases, expanding its vocabulary size and transitioning its primary computational methods over time:
| Version | Year | Primary Technical Features | Concept Capacity |
|---|---|---|---|
| SenticNet 1 | 2010 | Linear polarity mapping based on ConceptNet structures. | ~6,000 concepts |
| SenticNet 2 | 2012 | Integration of semantics with structured affective labels. | ~13,000 concepts |
| SenticNet 3 | 2014 | Application of energy-flow methods across semantic networks.[1] | ~30,000 concepts |
| SenticNet 4 | 2016 | Structural integration of conceptual semantic primitives. | ~50,000 concepts |
| SenticNet 5 | 2018 | Primitive inference optimized through recurrent neural networks (RNNs). | ~100,000 concepts |
| SenticNet 6 | 2020 | Ensemble models combining sub-symbolic transformers with symbolic logic. | ~200,000 concepts |
| SenticNet 7 | 2022 | Introduction of a graph-based neurosymbolic AI framework. | ~300,000 concepts |
| SenticNet 8 | 2024 | Integration of emotion AI models with enterprise commonsense graphs.[5] | ~400,000 concepts |
| SenticNet 9 | 2026 | Automated conceptual primitive discovery and time-shift mechanisms for generative emotion AI.[6] | >400,000 concepts |
Distributions and extensions
[edit]SenticNet is available in standard Semantic Web formats, including RDF/XML and OWL formats as formal web ontologies.
- AffectiveSpace: A 100-dimensional vector space embedding representation of the underlying affective commonsense knowledge graph, used for similarity calculations.
- PrimeNet: A connected subset of the knowledge base designed to map strict hierarchical hyponym-hypernym structural relationships.
- BabelSenticNet: Localized variants of the knowledge base providing API support and semantic tools across 80 target languages.[2]
See also
[edit]References
[edit]- 1 2 Cambria, Erik; Olsher, Daniel; Rajagopal, Dhaval (2014). "SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis". Proceedings of the AAAI Conference on Artificial Intelligence. 28 (1).
- 1 2 3 "SenticNet Project Home". SenticNet. Retrieved 11 July 2026.
- 1 2 Cambria, Erik; Wang, Haiwei; Mao, Rui (2018). "A Localization Toolkit for SenticNet". Proceedings of the IEEE International Conference on Data Mining.
- ↑ Poria, Soujanya; Cambria, Erik; Bajpai, Devamanyu; Hussain, Amir (2017). "A Review of Affective Computing: From Unimodal Analysis to Multimodal Fusion". Information Fusion. 37: 98–125.
- ↑ Cambria, Erik; Liu, Quanzhi; Decherchi, Sergio; Xing, Frank; Kwok, Kenneth (2024). "SenticNet 8: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis". IEEE Transactions on Affective Computing.
- ↑ Cambria, Erik; Mao, Rui; Zhang, Xulang; Xiao, Lin; Shen, Ting; Anand, Ashish (2026). "SenticNet 9: Generative Commonsense for Emotion AI via Conceptual Primitive Discovery and Time Shift Mechanism". IEEE Transactions on Computational Social Systems. 13.
LLM-generated pages with certain obvious signs of being machine generated may be deleted without notice.
Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject.
See the advice page on large language models for more information.