SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling (2025.findings-acl)
Copied to clipboard
| Challenge: | Traditional sentiment analysis methods focus on static reviews, failing to capture temporal relationship between user sentiment rating and textual content. |
| Approach: | They propose a dynamic graph-based framework that addresses data sparsity in streaming reviews. |
| Outcome: | The proposed framework reduces data sparsity by categorizing users into mid-tail, long-tail and extreme scenarios and incorporating LLM enhancements within a dynamic graph-based structure. |
Similar Papers
Sentiment Analysis on Streaming User Reviews via Dual-Channel Dynamic Graph Neural Network (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for sentiment analysis on user reviews neglect their time-varying characteristics. |
| Approach: | They propose a dual-channel framework that models temporal user and product dynamics for sentiment analysis. |
| Outcome: | The proposed framework is superior to existing methods on five real-world datasets. |
Dynamic and Multi-Channel Graph Convolutional Networks for Aspect-Based Sentiment Analysis (2021.findings-acl)
Copied to clipboard
| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
SentiStream: A Co-Training Framework for Adaptive Online Sentiment Analysis in Evolving Data Streams (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for online sentiment analysis rely on pre-existing datasets. |
| Approach: | They propose a co-training framework specifically designed for efficient sentiment analysis within dynamic data streams. |
| Outcome: | The proposed framework surpasses existing methods in terms of accuracy and computational efficiency. |
Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis (2023.findings-acl)
Copied to clipboard
| Challenge: | Typical approaches do not exploit the potential of historical reviews or do not make full use of user/product associations. |
| Approach: | They propose to use historical reviews to initialize user and product representations and incorporate textual associations via a user-product cross-context module. |
| Outcome: | The proposed method outperforms existing state-of-the-art models on IMDb, Yelp and Longformer benchmarks. |
From Graphs to Hypergraphs: Enhancing Aspect-Term Sentiment Analysis via Multi-Level Relational Modeling (2026.acl-srw)
Copied to clipboard
| Challenge: | Existing graph-based approaches to predict sentiment polarity for specific aspect terms rely on predefined pairwise structures to improve expressive capacity. |
| Approach: | They propose a dynamic hypergraph framework that can be used to generate a single instance-specific hypergraph from contextual token representations. |
| Outcome: | The proposed framework improves on Lap14, Rest14, and MAMS . it uses a single instance-specific hypergraph constructed directly from contextual token representations . |
Improving Document-Level Sentiment Analysis with User and Product Context (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. |
| Approach: | They propose to incorporate all available historical review text belonging to the author of the review in question and investigate the inclusion of his- torical reviews associated with the current product. |
| Outcome: | The proposed model improves on IMDB, Yelp 2013 and Yelpan 2014 datasets by more than 2 percentage points in the best case. |
DEEPER Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling (2025.acl-long)
Copied to clipboard
Aili Chen, Chengyu Du, Jiangjie Chen, Jinghan Xu, Yikai Zhang, Siyu Yuan, Zulong Chen, Liangyue Li, Yanghua Xiao
| Challenge: | Existing methods for generating personas from static historical data fail to capture dynamic behaviors and evolving preferences in real-world interactive scenarios. |
| Approach: | They propose a novel approach that iteratively updates personas using streaming user behavior data to continually enhance their quality. |
| Outcome: | The proposed approach delivers 32.2% reduction in user behavior prediction error over four update rounds, outperforming the best baseline by 22.92%. |
Towards Robust Sentiment Analysis of Temporally-Sensitive Policy-Related Online Text (2025.acl-srw)
Copied to clipboard
| Challenge: | Existing methods fail to adequately capture the temporal volatility inherent in policy-related sentiments, arguing that continuous time-series clustering and model merging achieve superior performance. |
| Approach: | They propose to use continuous time-series clustering to select data points for annotation based on temporal trends and then apply model merging techniques. |
| Outcome: | The proposed methods outperform existing methods by an average F1-score of 2.71% on temporally representative data. |
Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for aspect sentiment analysis do not include explicit sentiment expressions. |
| Approach: | They propose to construct a heterogeneous graph by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four benchmark datasets and significantly boosts performance in comparison with BERT. |
Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to predict sentiments on social media are limited and do not consider reciprocal influences among social media users. |
| Approach: | They propose a multi-perspective role-playing framework to simulate human response processes to extract sentiment-related features from social media messages. |
| Outcome: | The proposed model improves sentiment forecasting at microscopic and macroscopic levels. |