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TITLE Neurosymbolic Artificial Intelligence for Sentiment Analysis EDITORS Frank Xing, National University of Singapore, Singapore Iti Chaturvedi, James Cook University, Australia Erik Cambria, Nanyang Technological University, Singapore Amir Hussain, Edinburgh Napier University, UK Björn Schuller, audEERING GmbH, Germany JOURNAL IEEE Transactions on Affective Computing (impact factor: 10.506) CFP WEBLINK sentic.net/naisa.pdf<http://sentic.net/naisa.pdf> SUBMISSION LINK mc.manuscriptcentral.com/taffc-cs BACKGROUND AND MOTIVATION Artificial-intelligence driven models, especially deep learning models, have achieved state-of-the-art results for various natural language processing tasks including sentiment analysis. We get highly Neural network-based methods, especially deep learning, have been very successful in tackling the expanding data volume as we move into a digital age. Today, these methods are not only used for low-level cognitive tasks, such as recognizing objects and spotting keywords, but they have also been deployed in various industrial information systems to assist high-level decision-making in finance, education, and healthcare. While producing highly accurate predictions on datasets, those artifacts provide little understanding of the internal features and representations of the data. Although much effort has been devoted to opening the black-box of neural networks, e.g., sensitivity analysis, the interpretability problem generally worsens as the model complexity grows. The potentially broad societal impacts of neural network-based methods alert people to a dystopian future and re-ignite research on neurosymbolic AI: a key idea to mitigate unexpected model behavior and inject interpretability by combining learnable parameters (neuro-) with predefined knowledge templates (symbolic). Recent initiatives involve both the academia and company players, such as IBM and DeepMind. We have seen various types of logic parameterized and applied to such systems, including fuzzy logic, first-order logic, Boolean logic, and probabilistic logic. Instead of learning rules, another way of building neurosymbolic AI is to leverage existing knowledge bases and to fuse the information at some stage. Both ways have achieved sound improvements in sentiment analysis and deepened our understanding of affective computing and the cognitive root of human emotion. In such context, this special issue aims to further stimulate discussion on the design, use and evaluation of neurosymbolic AI, with an emphasis on human factors and societal implications. We invite theoretical work and review articles on practical use-cases of neurosymbolic AI that discuss sentiment analysis, emotion recognition, and social computing in general. Original works which help mediate and generate insights on human information behaviors, human-system interactions, and affective states with neural network-based models are also encouraged. TOPICS OF INTEREST - Neurosymbolic AI for sentiment and emotion analysis in social media - Linguistic knowledge in deep neural networks for sentiment analysis - Integrating knowledge for opinion mining - Aspect-based, multimodal, and multilingual aspects of sentiment analysis - Critical assessments of existing sentiment analysis methods - Explainable sentiment and emotion predictions - Theoretical foundations of neurosymbolic AI for affective computing - Commonsense reasoning for sentiment analysis - Sentic computing - Semantic models for affective computing - Phrase structure grammar for sentiment analysis - Conversational sentiment analysis - Joint sentiment analysis and sarcasm/irony detection - Sentiment analysis and language learning theory - Sentiment analysis and social network analysis - Sentiment analysis and stress/suicide detection - Sentiment analysis and forecasting methods TIMEFRAME Submission Deadline: 31st March, 2022 Peer Review Due: 1st July, 2022 Revision Due: 15th September, 2022 Final Decision: 1st November, 2022 For more CFPs on these topics, please follow SenticNet on socials To unsubscribe from this mailing list, simply reply UNSUBSCRIBE _____________________________ Erik Cambria, PhD, FIEEE Associate Professor & Provost Chair School of Computer Science & Engineering Nanyang Technological University, Singapore sentic.net fb.com/senticnet twitter.com/senticnet linkedin.com/company/senticnet [cid:C112E6BF-3C0C-49EE-8A13-3ED8654B0404@ntu.edu.sg] ________________________________ CONFIDENTIALITY: This email is intended solely for the person(s) named and may be confidential and/or privileged. If you are not the intended recipient, please delete it, notify us and do not copy, use, or disclose its contents. Towards a sustainable earth: Print only when necessary. Thank you.
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