SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods

Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1546–1556, Osaka, Japan, December 11-17 2016. SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods Marzieh Saeidi University College London msaeidi@cs.ucl.ac.uk Guillaume Bouchard Bloomsbury AI guillaume@bloomsbury.ai Maria Liakata University of Warwick m.liakata@warwick.ac.uk Sebastian Riedel University College London sriedel@cs.ucl.ac.uk Abstract 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. 1 Introduction Sentiment analysis is an important task in natural language processing. It has received not only a lot of interest in academia but also in industry, in particular for identifying customer satisfaction on products and services. Early research in the field (Das and Chen, 2001; Morinaga et al., 2002) of sentiment analysis only focused on identifying the overall sentiment or polarity of a given text. The underlying assumption of this work was that there is one overall polarity in the whole text. Aspect-based sentiment analysis (ABSA) (Jo and Oh, 2011; Pontiki et al., 2015; Pontiki et al., 2016) relates to the task of extracting fine-grained information by identifying the polarity towards different aspects of an entity in the same unit of text, and recognizing the polarity associated with each aspect separately. The datasets for this task were mostly based on specialized review platforms such as Yelp where it is assumed that only one entity is discussed in one review snippet, but the opinion on multiple aspects can be expressed. This task is particularly useful because a user can assess the aggregated senti- ment for each individual aspect of a given product or service and get a more fine-grained understanding of its quality. Another line of research in this field is targeted (a.k.a. target-dependent) sentiment analysis (Jiang et al., 2011; Vo and Zhang, 2015). Targeted sentiment analysis investigates the classification of opinion polarities towards certain target entity mentions in given sentences (often a tweet). For instance in the sentence “People everywhere love Windows & vista. Bill Gates”, polarity towards Bill Gates is “Neutral” but the positive sentiment towards Windows & vista will interfere with identifying it if the usual …

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