Effective inter-aspect words modeling for aspect-based sentiment analysis

TL;DR: This article proposed an unsupervised mathematical optimization framework to perform sentiment and emotion analysis of reviews, which achieved state-of-the-art results on two restaurant review datasets and validated and established the significance of the results through statistical analysis.

TL;DR: This study proposes PRCL-GCN, a novel aspect-based sentiment analysis method that combines prompted representation joint contrastive learning with graph convolutional networks to improve contextual representation and sentiment feature extraction.

TL;DR: A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.

TL;DR: Graph Attention Networks (GATs) as mentioned in this paper leverage masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.

TL;DR: This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.