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In this paper, we introduce the task of targeted aspect-based sentimentanalysis. The goal is to extract fine-grained information with respect toentities mentioned in user comments. This work extends both aspect-basedsentiment analysis that assumes a single entity per document and targetedsentiment analysis that assumes a single sentiment towards a target entity. Inparticular, we identify the sentiment towards each aspect of one or moreentities. As a testbed for this task, we introduce the SentiHood dataset,extracted from a question answering (QA) platform where urban neighbourhoodsare discussed by users. In this context units of text often mention severalaspects of one or more neighbourhoods. This is the first time that a genericsocial media platform in this case a QA platform, is used for fine-grainedopinion mining. Text coming from QA platforms is far less constrained comparedto text from review specific platforms which current datasets are based on. Wedevelop several strong baselines, relying on logistic regression andstate-of-the-art recurrent neural networks.

SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods