Absa

ABSA stands for Aspect-Based Sentiment Analysis, a subfield of natural language processing and sentiment analysis. It aims to identify opinions expressed about specific aspects or attributes of entities (such as a product or service) and determine the sentiment toward those aspects.

Typical tasks include locating aspect terms or categories in text, classifying the sentiment polarity (positive, negative, neutral) for each aspect, and sometimes summarizing aspect-level opinions. Some formulations also detect implicit aspects, relations between aspects, and compute sentiment strength.

Approaches to ABSA have evolved from feature-rich supervised methods to neural models. Modern ABSA relies on techniques such as sequence labeling, joint models for aspect and sentiment, attention mechanisms, and graph-based architectures. Pre-trained language models (such as BERT and RoBERTa) are commonly fine-tuned for ABSA. Many works use multi-task learning and transfer learning to improve performance in new domains or settings.

Datasets for ABSA include benchmark collections from SemEval, such as Rest14, Rest15, and Rest16 for restaurant reviews, and Laptop14/15/16 for electronics domains. Other domains like hotels and e-commerce have been explored. Evaluation metrics typically include F1 or accuracy for aspect detection and sentiment classification, as well as macro-averaged sentiment accuracy.

Applications of ABSA include customer feedback analytics, market research, product improvement, and reputation management. ABSA provides per-aspect insights rather than only an overall sentiment score, enabling more targeted understanding of user opinions and more actionable business decisions.

Challenges in ABSA include handling overlapping or hierarchical aspects, multi-aspect sentences, domain adaptation, noisy or sarcastic text, and implicit aspects, as well as annotation costs and the need for model explainability.