# A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis

arXiv: https://arxiv.org/abs/1609.02745 · HTML: https://mlsanity.com/paper/1609.02745/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2016.

## Built on by (most cited first, 21 of 21)

- [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://mlsanity.com/paper/2110.00796/) (2021)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa](https://mlsanity.com/paper/2104.04986/) (2021)
- [Solving Aspect Category Sentiment Analysis as a Text Generation Task](https://mlsanity.com/paper/2110.07310/) (2021)
- [ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction](https://mlsanity.com/paper/2103.06605/) (2021)
- [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022)
- [SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence](https://mlsanity.com/paper/2105.15079/) (2021)
- [Iterative Hierarchical Attention for Answering Complex Questions over Long Documents](https://mlsanity.com/paper/2106.00200/) (2021)
- [Deep Learning-based Sentiment Classification: A Comparative Survey](https://mlsanity.com/paper/2312.17253/) (2023)
- [Arabic aspect based sentiment analysis using bidirectional GRU based models](https://mlsanity.com/paper/2101.10539/) (2021)
- [Hyperbolic Disentangled Representation for Fine-Grained Aspect Extraction](https://mlsanity.com/paper/2112.09215/) (2021)
- [Preliminary Steps Towards Federated Sentiment Classification](https://mlsanity.com/paper/2107.11956/) (2021)
- [Arabic aspect sentiment polarity classification using BERT](https://mlsanity.com/paper/2107.13290/) (2021)
- [Stock Market Prediction using Natural Language Processing -- A Survey](https://mlsanity.com/paper/2208.13564/) (2022)
- [Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings](https://mlsanity.com/paper/2606.04286/) (2026)
- [A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian Reviews](https://mlsanity.com/paper/2201.06313/) (2022)
- [Pretrained Language Encoders are Natural Tagging Frameworks for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2208.09617/) (2022)
- [Target-Dependent Multimodal Sentiment Analysis Via Employing Visual-to Emotional-Caption Translation Network using Visual-Caption Pairs](https://mlsanity.com/paper/2408.10248/) (2024)
- [CPR: Leveraging LLMs for Topic and Phrase Suggestion to Facilitate Comprehensive Product Reviews](https://mlsanity.com/paper/2504.13993/) (2025)
- [Domain-Adaptive Pre-Training for Arabic Aspect-Based Sentiment Analysis: A Comparative Study of Domain Adaptation and Fine-Tuning Strategies](https://mlsanity.com/paper/2509.16788/) (2025)

## Abstract (arXiv)

Opinion mining from customer reviews has become pervasive in recent years. Sentences in reviews, however, are usually classified independently, even though they form part of a review's argumentative structure. Intuitively, sentences in a review build and elaborate upon each other; knowledge of the review structure and sentential context should thus inform the classification of each sentence. We demonstrate this hypothesis for the task of aspect-based sentiment analysis by modeling the interdependencies of sentences in a review with a hierarchical bidirectional LSTM. We show that the hierarchical model outperforms two non-hierarchical baselines, obtains results competitive with the state-of-the-art, and outperforms the state-of-the-art on five multilingual, multi-domain datasets without any hand-engineered features or external resources.