# Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis

arXiv: https://arxiv.org/abs/1603.06679 · HTML: https://mlsanity.com/paper/1603.06679/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2016.

## Built on by (most cited first, 30 of 35)

- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2103.07665/) (2021)
- [Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2111.02194/) (2021)
- [Structured Sentiment Analysis as Dependency Graph Parsing](https://mlsanity.com/paper/2105.14504/) (2021)
- [Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis](https://mlsanity.com/paper/2203.10796/) (2022)
- [Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2210.06629/) (2022)
- [A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2204.12674/) (2022)
- [The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis](https://mlsanity.com/paper/2310.12318/) (2023)
- [DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect Extraction](https://mlsanity.com/paper/2109.00571/) (2021)
- [Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations](https://mlsanity.com/paper/2204.10714/) (2022)
- [UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning](https://mlsanity.com/paper/2211.10986/) (2022)
- [A Comprehensive Survey on Affective Computing; Challenges, Trends, Applications, and Future Directions](https://mlsanity.com/paper/2305.07665/) (2023)
- [A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task](https://mlsanity.com/paper/2103.15255/) (2021)
- [Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2106.03806/) (2021)
- [PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2208.01368/) (2022)
- [Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs](https://mlsanity.com/paper/2210.10144/) (2022)
- [MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2306.16956/) (2023)
- [Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2406.18078/) (2024)
- [Hyperbolic Disentangled Representation for Fine-Grained Aspect Extraction](https://mlsanity.com/paper/2112.09215/) (2021)
- [Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE)](https://mlsanity.com/paper/2103.05815/) (2021)
- [Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction](https://mlsanity.com/paper/2208.08280/) (2022)
- [A Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning](https://mlsanity.com/paper/2403.17254/) (2024)
- [Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2211.05457/) (2022)
- [A Better Choice: Entire-space Datasets for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2212.09052/) (2022)
- [Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing](https://mlsanity.com/paper/2407.04801/) (2024)
- [A Scalable Unsupervised Framework for multi-aspect labeling of Multilingual and Multi-Domain Review Data](https://mlsanity.com/paper/2505.09286/) (2025)
- [FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis](https://mlsanity.com/paper/2505.21040/) (2025)
- [Extracting all Aspect-polarity Pairs Jointly in a Text with Relation Extraction Approach](https://mlsanity.com/paper/2109.00256/) (2021)
- [Aspect-Sentiment-Multiple-Opinion Triplet Extraction](https://mlsanity.com/paper/2110.07303/) (2021)

## Abstract (arXiv)

In aspect-based sentiment analysis, extracting aspect terms along with the opinions being expressed from user-generated content is one of the most important subtasks. Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task. In this paper, we propose a novel joint model that integrates recursive neural networks and conditional random fields into a unified framework for explicit aspect and opinion terms co-extraction. The proposed model learns high-level discriminative features and double propagate information between aspect and opinion terms, simultaneously. Moreover, it is flexible to incorporate hand-crafted features into the proposed model to further boost its information extraction performance. Experimental results on the SemEval Challenge 2014 dataset show the superiority of our proposed model over several baseline methods as well as the winning systems of the challenge.