# SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment

arXiv: https://arxiv.org/abs/2411.18162 · HTML: https://mlsanity.com/paper/2411.18162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://mlsanity.com/paper/2104.08773/) (2021)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Recognize Anything: A Strong Image Tagging Model](https://mlsanity.com/paper/2306.03514/) (2023)
- [MemNet: A Persistent Memory Network for Image Restoration](https://mlsanity.com/paper/1708.02209/) (2017)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks](https://mlsanity.com/paper/1708.04299/) (2017)
- [DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition](https://mlsanity.com/paper/2012.08695/) (2020)
- [Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module](https://mlsanity.com/paper/2209.02604/) (2022)
- [Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space Translation](https://mlsanity.com/paper/2108.01682/) (2021)
- [XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection](https://mlsanity.com/paper/2011.01612/) (2020)
- [Can Large Language Models Capture Dissenting Human Voices?](https://mlsanity.com/paper/2305.13788/) (2023)
- [Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2210.08713/) (2022)
- [Multimodal Multi-loss Fusion Network for Sentiment Analysis](https://mlsanity.com/paper/2308.00264/) (2023)
- [CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI](https://mlsanity.com/paper/2205.14727/) (2022)
- [Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations](https://mlsanity.com/paper/2306.01505/) (2023)
- [EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation](https://mlsanity.com/paper/2303.11117/) (2023)
- [Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention](https://mlsanity.com/paper/2305.00262/) (2023)
- [Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation](https://mlsanity.com/paper/2207.13254/) (2022)

## Abstract (arXiv)

With strong expressive capabilities in Large Language Models(LLMs), generative models effectively capture sentiment structures and deep semantics, however, challenges remain in fine-grained sentiment classification across multi-lingual and complex contexts. To address this, we propose the Sentiment Cross-Lingual Recognition and Logic Framework (SentiXRL), which incorporates two modules,an emotion retrieval enhancement module to improve sentiment classification accuracy in complex contexts through historical dialogue and logical reasoning,and a self-circulating analysis negotiation mechanism (SANM)to facilitates autonomous decision-making within a single model for classification tasks.We have validated SentiXRL's superiority on multiple standard datasets, outperforming existing models on CPED and CH-SIMS,and achieving overall better performance on MELD,Emorynlp and IEMOCAP. Notably, we unified labels across several fine-grained sentiment annotation datasets and conducted category confusion experiments, revealing challenges and impacts of class imbalance in standard datasets.