Guest Editorial Neurosymbolic AI for Sentiment Analysis

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.