# SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods

arXiv: https://arxiv.org/abs/1610.03771 · HTML: https://mlsanity.com/paper/1610.03771/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2016.

## Built on by (most cited first, 30 of 32)

- [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022)
- [A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends](https://mlsanity.com/paper/2311.10777/) (2023)
- [On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation](https://mlsanity.com/paper/2106.04753/) (2021)
- [The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis](https://mlsanity.com/paper/2310.12318/) (2023)
- [Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence](https://mlsanity.com/paper/2402.10073/) (2024)
- [Challenges for Open-domain Targeted Sentiment Analysis](https://mlsanity.com/paper/2204.06893/) (2022)
- [Learning a Better Initialization for Soft Prompts via Meta-Learning](https://mlsanity.com/paper/2205.12471/) (2022)
- [Entity Framing and Role Portrayal in the News](https://mlsanity.com/paper/2502.14718/) (2025)
- [BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis](https://mlsanity.com/paper/2203.11702/) (2022)
- [It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance](https://mlsanity.com/paper/2405.20703/) (2024)
- [Czech Dataset for Complex Aspect-Based Sentiment Analysis Tasks](https://mlsanity.com/paper/2508.08125/) (2025)
- [Targeted aspect based multimodal sentiment analysis:an attention capsule extraction and multi-head fusion network](https://mlsanity.com/paper/2103.07659/) (2021)
- [Survey of Aspect-based Sentiment Analysis Datasets](https://mlsanity.com/paper/2204.05232/) (2022)
- [DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning](https://mlsanity.com/paper/2205.12662/) (2022)
- [UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt](https://mlsanity.com/paper/2309.11065/) (2023)
- [Prompt-Based Approach for Czech Sentiment Analysis](https://mlsanity.com/paper/2508.08651/) (2025)
- [Weakly Supervised Text Classification on Free Text Comments in Patient-Reported Outcome Measures](https://mlsanity.com/paper/2308.06199/) (2023)
- [Leveraging Explainable AI to Analyze Researchers' Aspect-Based Sentiment about ChatGPT](https://mlsanity.com/paper/2308.11001/) (2023)
- [Geo-located Aspect Based Sentiment Analysis (ABSA) for Crowdsourced Evaluation of Urban Environments](https://mlsanity.com/paper/2312.12253/) (2023)
- [CERM: Context-aware Literature-based Discovery via Sentiment Analysis](https://mlsanity.com/paper/2402.01724/) (2024)
- [GERestaurant: A German Dataset of Annotated Restaurant Reviews for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2408.07955/) (2024)
- [EmoGRACE: Aspect-based emotion analysis for social media data](https://mlsanity.com/paper/2503.15133/) (2025)
- [Cross-lingual Aspect-Based Sentiment Analysis: A Survey on Tasks, Approaches, and Challenges](https://mlsanity.com/paper/2508.09516/) (2025)
- [Representation of professions in entertainment media: Insights into frequency and sentiment trends through computational text analysis](https://mlsanity.com/paper/2110.03873/) (2021)
- [Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?](https://mlsanity.com/paper/2111.05139/) (2021)
- [BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification](https://mlsanity.com/paper/2210.15225/) (2022)
- [Supervised Gradual Machine Learning for Aspect Category Detection](https://mlsanity.com/paper/2404.05245/) (2024)
- [A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models](https://mlsanity.com/paper/2508.17994/) (2025)
- [QLENS: Towards A Quantum Perspective of Language Transformers](https://mlsanity.com/paper/2510.11963/) (2025)
- [Extending Czech Aspect-Based Sentiment Analysis with Opinion Terms: Dataset and LLM Benchmarks](https://mlsanity.com/paper/2602.22730/) (2026)

## Abstract (arXiv)

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.