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# SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods
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Authors: Marzieh Saeidi, Guillaume Bouchard, Maria Liakata, Sebastian Riedel
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**Disclaimer: All rights belong to the original authors.**
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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 neighborhoods are discussed by users. In this context units of text often mention several aspects of one or more neighborhoods. 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 on which current datasets are based. We develop several strong baselines, relying on logistic regression and state-of-the-art recurrent neural networks.
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---
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annotations_creators:
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- crowdsourced
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language_creators:
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- crowdsourced
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languages:
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- en
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- ''
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licenses: []
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multilinguality:
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- monolingual
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pretty_name: SentiHood Dataset
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size_categories:
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- unknown
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source_datasets:
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- original
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task_categories:
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- text-classification
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task_ids:
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- sentiment-classification
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- multi-class-classification
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- natural-language-inference
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---
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# SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods
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Authors: Marzieh Saeidi, Guillaume Bouchard, Maria Liakata, Sebastian Riedel
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**Disclaimer: All rights belong to the original authors.**
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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 neighborhoods are discussed by users. In this context units of text often mention several aspects of one or more neighborhoods. 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 on which current datasets are based. We develop several strong baselines, relying on logistic regression and state-of-the-art recurrent neural networks.
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