Xing, Frank, Schuller, Bjorn, Chaturvedi, Iti, Cambria, Erik, and Hussain, Amir (2023) Guest Editorial Neurosymbolic AI for Sentiment Analysis. IEEE Transactions on Affective Computing, 14 (3). pp. 1711-1715.

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Abstract

Neural network-based methods, especially deep learning, have been a burgeoning area in AI research and have been successful in tackling the expanding data volume as we move into a digital age. Today, the neural network-based 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 natural language processing, there have been two milestones for the past decade: one is word2vec [1], a group of neural models that learn word embeddings (vector representations of words) from large datasets; and one is the most recent GPT-based models [2], which combine reinforcement learning with a generative transformer in order to enable multi-round end-to-end conversations. While producing highly accurate predictions on datasets and generating human-like utterances, those neural network-based artifacts provide little understanding of the internal features and representations of the data. Many problems and concerns subsequently emerge from this black-box issue. Because some of the problems and concerns are also relevant in the context of sentiment analysis.

Item ID:	80789
Item Type:	Article (Editorial)
ISSN:	1949-3045
Related URLs:	Publisher
Copyright Information:	© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Date Deposited:	02 Nov 2023 03:27
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 40% 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 35% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460206 Knowledge representation and reasoning @ 25%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220408 Information systems @ 30% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 20%
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