Guest Editorial Neurosymbolic AI for Sentiment Analysis
Journal Contribution ResearchOnline@JCUAbstract
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.
Journal
IEEE Transactions on Affective Computing
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Volume
14
ISBN/ISSN
1949-3045
Edition
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Issue
3
Pages Count
5
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Publisher
Institute of Electrical and Electronics Engineers
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Publisher Location
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Publish Date
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Url
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Date
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EISSN
N/A
DOI
10.1109/TAFFC.2023.3310856