AI Empowered Sentiment Analysis

<p>With the popularity of the social media a large amount of user-generated content such as comments is emerging which is crucial for all industries. Recently the development of deep learning and computing power have made it possible to handle complex data. However there are still some including (but are not limited to): (1) How can we construct a multi-modal sentiment analysis framework? (2) How can we accurately extract aspect-sentiment quadruples? (3) How can we generate fine-grained sentiment text? To tackle these challenges this Special Issue focuses on multi-modal sentiment analysis aspect-sentiment extraction interpretability and so on. In the following we briefly summarize the selected two papers that we believe will make significant contributions. (1) Generative Aspect Sentiment Quad Prediction with Self-Inference Template by Li et al. considered that current research predominantly confines templates to single sentences limiting the model's reasoning opportunities. Therefore the authors introduce a self-inference template (SIT) to guide the model in thoughtful reasoning. (2) Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction by Sun et al. proposes a new sentiment cue extraction (SCE) self-supervised framework aimed at improving the interpretability of models. In conclusion we extend our heartfelt appreciation to all the authors and reviewers who selflessly put their energy to ensure the successful completion of this Special Issue.</p>