The paper discusses the SemEval-2015 Task 12, which aimed to advance research in Aspect Based Sentiment Analysis (ABSA) beyond sentence or text-level sentiment classification. The task focused on identifying opinions expressed about specific entities and their aspects, using manually annotated reviews from three domains: restaurants, laptops, and hotels. The evaluation procedure included two phases: Phase A required participants to identify aspect categories and opinion target expressions (OTE) for the restaurants domain, and only aspect categories for the laptops domain. Phase B involved predicting sentiment polarity for the same domains. The task attracted 93 submissions from 16 teams, with the best performance achieved by the NLANGP team in the laptops domain and the Sentivue team in the restaurants domain. The paper also provides an overview of the task setup, datasets, evaluation measures, and baseline methods, highlighting the challenges and future directions in ABSA.The paper discusses the SemEval-2015 Task 12, which aimed to advance research in Aspect Based Sentiment Analysis (ABSA) beyond sentence or text-level sentiment classification. The task focused on identifying opinions expressed about specific entities and their aspects, using manually annotated reviews from three domains: restaurants, laptops, and hotels. The evaluation procedure included two phases: Phase A required participants to identify aspect categories and opinion target expressions (OTE) for the restaurants domain, and only aspect categories for the laptops domain. Phase B involved predicting sentiment polarity for the same domains. The task attracted 93 submissions from 16 teams, with the best performance achieved by the NLANGP team in the laptops domain and the Sentivue team in the restaurants domain. The paper also provides an overview of the task setup, datasets, evaluation measures, and baseline methods, highlighting the challenges and future directions in ABSA.