README.md

metadata

annotations_creators: []
language_creators: []
language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
pretty_name: SentiHood  Dataset
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
  - multi-class-classification
  - natural-language-inference

Dataset Card for [SentiHood]

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Paper: https://arxiv.org/abs/1610.03771
• Leaderboard: https://paperswithcode.com/sota/aspect-based-sentiment-analysis-on-sentihood

Dataset Summary

Created as a part of the paper "SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods" by Saeidi et al.

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 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.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

Monolingual (only English)

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

Thanks to @Bhavnicksm for adding this dataset.