Developing a multimodal and multilingual model for accurate sentiment analysis of tweets by leveraging textual and visual features.
SentiCSE proposes a novel sentiment-aware pre-training framework that ensures high-quality sentiment representations by leveraging sentiment-related linguistic knowledge through combined word-level and sentence-level objectives.
FaiMA proposes a novel framework utilizing in-context learning for multi-domain ABSA tasks, achieving significant performance improvements.
マガヒ・ヒンディー語・英語(MHE)コード混合言語の感情分析用の新しいデータセット、MaCMSを紹介します。
The Extensible Multi-Granularity Fusion (EMGF) network integrates diverse linguistic and structural features efficiently, resulting in superior performance in Aspect-Based Sentiment Analysis (ABSA).