Despite the impressive capabilities of large language models (LLMs) in aspect-based sentiment analysis (ABSA), the role of syntactic information remains underexplored in LLMs. Syntactic structures are known to be crucial for capturing aspect-opinion relationships. To explore whether LLMs can effectively leverage syntactic information to improve ABSA performance, we propose a novel multi-step reasoning framework, the Syntax-Opinion-Sentiment Reasoning Chain (Syn-Chain). Syn-Chain sequentially analyzes syntactic dependencies, extracts opinions, and classifies sentiment. We introduce Syn-Chain into LLMs via zero-shot prompting, and results show that Syn-Chain significantly enhances ABSA performance, though smaller LLM exhibit weaker performance. Furthermore, we enhance smaller LLMs via distillation using GPT-3.5-generated Syn-Chain responses, achieving state-of-the-art ABSA performance. Our findings highlight the importance of syntactic information for improving LLMs in ABSA and offer valuable insights for future research.

The SemEval 2025 Task 10 Subtask2 presents a multi-task multi-label text classification challenge. The task requires systems to classify documents simultaneously across three distinct topics, the Climate Change(CC), the Ukraine Russia War(URW), and others. Several challenge were identified, including the instinct distinct of topics, the imbalance of categories, the insufficient samples, and the different distribution of develop set and test set. To address these challenges, two deep learning model have been implemented. One of the approach is the Contrastive learning augmented Cascaded UNet model(CCU), which employs a cascaded architecture to jointly process all subtasks. This model incorporates an UNet-style architecture to classify embeddings extracted by the base text encoder. A domain adaption method was implemented to facilitate joint learning across different document topics. We address the data insufficiency through contrastive learning and mitigate data imbalance using asymmetric loss function. We also implemented a shallow machine learning model. In this approach, transformer encoder models were applied to extract text embedding from various aspect, then deploy machine learning method to do the classification and compared with the base line. The UNet-style model achieves the highest f1 sample at 0.365 on the test set of 5th place compared with all approaches on leader board. Our source code developed for this paper are available at

In this study, we introduce an MLP approach for extracting multimodal cause utterances in conversations, utilizing the multimodal conversational emotion causes from the ECF dataset. Our research focuses on evaluating a bi-modal framework that integrates video and audio embeddings to analyze emotional expressions within dialogues. The core of our methodology involves the extraction of embeddings from pre-trained models for each modality, followed by their concatenation and subsequent classification via an MLP network. We compared the accuracy performances across different modality combinations including text-audio-video, video-audio, and audio only.