SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods

Marzieh Saeidi, Guillaume Bouchard, Maria Liakata, Sebastian Riedel


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,i.e. QA, is used for fine-grained opinion mining. Text coming from QA platforms are 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
Anthology ID:
C16-1146
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1546–1556
Language:
URL:
https://aclanthology.org/C16-1146
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Bibkey:
Cite (ACL):
Marzieh Saeidi, Guillaume Bouchard, Maria Liakata, and Sebastian Riedel. 2016. SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1546–1556, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods (Saeidi et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1146.pdf