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