The following publications are possibly variants of this publication:
- SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual PrimitivesErik Cambria, Soujanya Poria, Rajiv Bajpai, Björn W. Schuller. COLING 2016: 2666-2677 [doi]
- Developing a Concept-Level Knowledge Base for Sentiment Analysis in SinglishRajiv Bajpai, Danyuan Ho, Erik Cambria. cicling 2018: 347-361 [doi]
- SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment AnalysisErik Cambria, Catherine Havasi, Amir Hussain. flairs 2012: [doi]
- Sentiment-Oriented Information Retrieval: Affective Analysis of Documents Based on the SenticNet FrameworkFederica Bisio, Claudia Meda, Paolo Gastaldo, Rodolfo Zunino, Erik Cambria. In Witold Pedrycz, Shyi-Ming Chen, editors, Sentiment Analysis and Ontology Engineering - An Environment of Computational Intelligence. Volume 639 of Studies in Computational Intelligence, pages 175-197, Springer, 2016. [doi]
- SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment AnalysisErik Cambria, Qian Liu, Sergio Decherchi, Frank Xing, Kenneth Kwok. lrec 2022: 3829-3839 [doi]
- SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment AnalysisErik Cambria, Daniel Olsher, Dheeraj Rajagopal. AAAI 2014: 1515-1521 [doi]
- SenticNet: A Publicly Available Semantic Resource for Opinion MiningErik Cambria, Robert Speer, Catherine Havasi, Amir Hussain. aaaifs 2010: [doi]
- SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment AnalysisErik Cambria, Yang Li 0055, Frank Z. Xing, Soujanya Poria, Kenneth Kwok. CIKM 2020: 105-114 [doi]