Techniques and developments in aspect-based sentiment analysis

Determining the sentiment polarity towards certain textual pieces is a crucial problem in natural language processing, known as aspect-based sentiment analysis (ABSA). This paper offers a comprehensive review of the recent advancements and trends in ABSA research. The history and importance of ABSA are first discussed, and then each of its subtasks—aspect word extraction, aspect category identification, aspect sentiment classification, and aspect sentiment intensity prediction—is thoroughly defined. The paper then systematically summarizes the main research methods in ABSA, categorizing them into rule-based, machine learning-based, deep learning-based, unsupervised and semi-supervised learning, and cross-domain and cross-lingual approaches. For each category, the paper discusses the representative techniques, such as dependency parsing, feature engineering, attention mechanism, graph neural networks, and domain adaptation. Furthermore, the paper highlights the commonly used datasets, evaluation metrics, and methods in ABSA research. It also discusses the application scenarios of ABSA in various domains, such as restaurants, e-commerce, and tourism, as well as its potential in public opinion monitoring and reputation management. Finally, the paper identifies the technical challenges and future research directions in ABSA, including multimodal, multilingual, and cross-domain ABSA, fine-grained sentiment analysis, sentiment cause identification, and the integration of commonsense knowledge and causal reasoning. This review aims to provide a valuable reference for researchers and practitioners interested in ABSA and to promote further advancements in this field.