Sentiment analysis (SA) aims to understand the attitudes and views of opinion holders with
computers. Previous studies have achieved significant breakthroughs and extensive …

User-generated content on various Internet platforms is growing explosively, and contains
valuable information that helps decision-making. However, extracting this information …

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 …

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a
better understanding of the human language for linguistic-based human-computer …

Dependency trees have been intensively used with graph neural networks for aspect-based
sentiment classification. Though being effective, such methods rely on external dependency …

Aspect-level sentiment classification still remains a challenge: how to capture contextual
semantic correlation between aspect word and content words more effectively. LSTM-SNP is …

Aspect-level sentiment analysis aims to recognize the sentiment polarity of an aspect or a
target in a comment. Recently, graph convolutional networks based on linguistic …

We introduce InstructABSA, an instruction learning paradigm for Aspect-Based Sentiment
Analysis (ABSA) subtasks. Our method introduces positive, negative, and neutral examples …

Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific
aspect in the given sentence. While pre-trained language models such as BERT have …

This paper proposes human-interpretable learning of aspect-based sentiment analysis
(ABSA), employing the recently introduced Tsetlin Machines (TMs). We attain interpretability …