Aspectbased

Aspect-based sentiment analysis, often referred to as ABSA, is a subfield of sentiment analysis that aims to identify opinions about specific aspects or features of entities in text. It seeks to determine not only the overall sentiment but also the sentiment expressed toward individual aspects, the terms describing those aspects, and the target of the opinion. This approach provides fine-grained insight into customer opinions and supports analysis at the feature level rather than only at the document level.

Core tasks in ABSA include aspect term extraction (ATE), which identifies facet terms in text; aspect category detection (ACD), which assigns aspects to predefined categories; and sentiment polarity classification for each aspect (SPC), which labels the sentiment toward each identified aspect. Some implementations also perform aspect-based opinion summarization or sentiment strength estimation. Methods range from rule-based and lexicon-driven approaches to supervised learning and neural networks, including sequence labeling, dependency parsing, and transformer-based models such as BERT. Datasets typically consist of sentences or reviews annotated with aspect terms, categories, and sentiment labels. Evaluation uses F1 for extraction tasks and accuracy or macro-averaged F1 for classification.

Applications include product reviews, hospitality and travel feedback, and social media monitoring, where stakeholders want feature-level sentiment insights (for example, praising battery life while criticizing price). Common ABSA benchmarks come from SemEval tasks in restaurant and laptop domains, alongside domain-specific review datasets. Challenges include detecting implicit aspects, handling multi-word or synonymous aspect terms, cross-domain transfer, sarcasm, and multilingual ABSA. Ongoing work explores joint models that combine extraction and sentiment prediction, as well as cross-lingual ABSA to broaden applicability.