Aspect-based sentiment analysis (ABSA) is the task of identifying sentiment expressed about a given aspect in a text document. It is a finer-grained task than traditional sentiment analysis, which is typically concerned only with the document-level or sentence-level sentiment.
ABSA is often performed in the context of opinion mining, which is the task of extracting opinions, attitudes, and emotions from text. Aspect-based sentiment analysis can be used to understand the attitudes and emotions of a writer with respect to specific entities or topics in a text document. For example, ABSA can be used to analyze customer reviews of products to understand what features of the product are most important to customers, or to analyze reviews of a hotel to understand what aspects of the hotel are most important to guests.
ABSA can be performed using a variety of methods, including rule-based methods, supervised learning methods, and unsupervised learning methods. Rule-based methods typically involve manually creating a set of rules that are used to identify sentiment-bearing words or phrases in a text document. Supervised learning methods involve training a machine learning classifier on a labeled dataset of sentiment-annotated text documents. Unsupervised learning methods can be used to identify sentiment-bearing words or phrases in a text document without the need for training data.
ABSA is a challenging task due to the complexities of human language. For example, the same word can have different sentiment connotations depending on the context in which it is used. Aspect-based sentiment analysis requires a deep understanding of human language in order to be performed accurately.