Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis
(ABSA), aiming to analyze and understand people's opinions at the aspect level, has been …

The emergence of various e-commerce sites has led to an increase in review sites for
various services and products. People nowadays easily get information about products and …

Graph Attention Networks (GATs) are one of the most popular GNN architectures and are
considered as the state-of-the-art architecture for representation learning with graphs. In …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Aspect-based sentiment analysis is a fine-grained sentiment analysis task, which needs to
detection the sentiment polarity towards a given aspect. Recently, graph neural models over …

Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific
aspect in online reviews. Most recent efforts adopt attention-based neural network models to …

The state-of-the-art methods in aspect-level sentiment classification have leveraged the
graph based models to incorporate the syntactic structure of a sentence. While being …

In this paper, we investigate the modeling power of contextualized embeddings from pre-
trained language models, eg BERT, on the E2E-ABSA task. Specifically, we build a series of …

Aspect-based Sentiment Analysis (ABSA), aiming at predicting the polarities for aspects, is a
fine-grained task in the field of sentiment analysis. Previous work showed syntactic …