---
title: "FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis"
authors:
  - "Wei Chen"
  - "Zhao Zhang"
  - "Meng Yuan"
  - "Kepeng Xu"
  - "Fuzhen Zhuang"
doi: "10.24963/ijcai.2025/304"
url: "https://ijcai.org/proceedings/2025/304"
pdf: "https://ijcai.org/proceedings/2025/0304.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "2731-2739"
track: "Main Track"
keywords:
  - "Data Mining: DM: Mining text, web, social media"
  - "Knowledge Representation and Reasoning: KRR: Knowledge representation languages"
  - "Machine Learning: ML: Deep learning architectures"
---

# FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis

Wei Chen, Zhao Zhang, Meng Yuan, Kepeng Xu, Fuzhen Zhuang

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 2731-2739. DOI: [10.24963/ijcai.2025/304](https://doi.org/10.24963/ijcai.2025/304)

## Abstract

In this paper, we address the task of targeted sentiment analysis , which involves two sub-tasks, i.e., identifying specific aspects from reviews and determining their corresponding senti-ments.  Aspect extraction forms the foundation for sentiment prediction, highlighting the critical dependency between these two tasks for effective cross-task knowledge transfer.
	While most existing studies adopt a multi-task learning paradigm to align task-specific features in the latent space, they predominantly rely on coarse-grained knowledge transfer. Such approaches lack fine-grained control over aspect-sentiment relationships, often assuming uniform sentiment polarity within related aspects. This oversimplification neglects contextual cues that differentiate sentiments, leading to negative transfer.
   To overcome these limitations, we propose FCKT, a fine-grained cross-task knowledge transfer framework tailored for TSA. By explicitly incorporating aspect-level information into sentiment prediction, our framework achieves fine-grained knowledge transfer, effectively mitigating negative transfer and enhancing task performance.
   Extensive experiments on three real-world datasets, including comparisons with various baselines and large language models (LLMs), demonstrate the effectiveness of FCKT. The source code
is available on https://github.com/cwei01/FCKT.