SemEval-2015 Task 12 aimed to advance research in Aspect Based Sentiment Analysis (ABSA), moving beyond sentence-level sentiment classification. The task built upon SemEval-2014 Task 4 and provided manually annotated reviews in three domains: restaurants, laptops, and hotels. It attracted 93 submissions from 16 teams. The goal was to identify opinions about specific entities and their aspects, such as the price of a laptop or the quality of food in a restaurant. The task included subtasks for in-domain and out-of-domain ABSA, with the latter involving hotel reviews where no training data was available. The task introduced a unified framework for ABSA, defining aspect categories as combinations of entity types and attribute types. This framework allowed for more precise identification of entities and their attributes. The datasets included reviews from three domains, with annotations for aspect categories, opinion target expressions, and sentiment polarity. The evaluation measures included F-1 scores for aspect category and OTE extraction, and accuracy for sentiment polarity. The task involved three main phases: Phase A focused on extracting aspect categories and OTEs, while Phase B evaluated sentiment polarity. Baselines were provided, including SVM-based models and majority baselines. The results showed that systems performed better in the restaurants domain due to its less fine-grained classification schema. The best performance was achieved by the NLANGP team in the laptops domain, while Sentiue performed well in both domains. The task provided benchmark datasets and baselines for the respective subtasks, contributing to the advancement of ABSA research. Future work includes applying the framework to other languages and incorporating additional information such as topics, events, and linguistic phenomena. The task highlighted the importance of accurate annotation and the challenges of handling out-of-domain ABSA.SemEval-2015 Task 12 aimed to advance research in Aspect Based Sentiment Analysis (ABSA), moving beyond sentence-level sentiment classification. The task built upon SemEval-2014 Task 4 and provided manually annotated reviews in three domains: restaurants, laptops, and hotels. It attracted 93 submissions from 16 teams. The goal was to identify opinions about specific entities and their aspects, such as the price of a laptop or the quality of food in a restaurant. The task included subtasks for in-domain and out-of-domain ABSA, with the latter involving hotel reviews where no training data was available. The task introduced a unified framework for ABSA, defining aspect categories as combinations of entity types and attribute types. This framework allowed for more precise identification of entities and their attributes. The datasets included reviews from three domains, with annotations for aspect categories, opinion target expressions, and sentiment polarity. The evaluation measures included F-1 scores for aspect category and OTE extraction, and accuracy for sentiment polarity. The task involved three main phases: Phase A focused on extracting aspect categories and OTEs, while Phase B evaluated sentiment polarity. Baselines were provided, including SVM-based models and majority baselines. The results showed that systems performed better in the restaurants domain due to its less fine-grained classification schema. The best performance was achieved by the NLANGP team in the laptops domain, while Sentiue performed well in both domains. The task provided benchmark datasets and baselines for the respective subtasks, contributing to the advancement of ABSA research. Future work includes applying the framework to other languages and incorporating additional information such as topics, events, and linguistic phenomena. The task highlighted the importance of accurate annotation and the challenges of handling out-of-domain ABSA.