SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods
arXiv:1610.03771
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.
Accepted at COLING 2016
Cited by in corpus (9)
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- Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models
- On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation
- Targeted aspect based multimodal sentiment analysis:an attention capsule extraction and multi-head fusion network
- A Multi-Task Incremental Learning Framework with Category Name Embedding for Aspect-Category Sentiment Analysis
- Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?
- Context-aware Embedding for Targeted Aspect-based Sentiment Analysis