Multimodal and Multilingual Model for Sentiment Analysis of Tweets
Developing a multimodal and multilingual model for accurate sentiment analysis of tweets by leveraging textual and visual features.
SentiCSE: A Sentiment-aware Contrastive Sentence Embedding Framework for Constructing High-quality Sentiment Representations
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: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis
FaiMA proposes a novel framework utilizing in-context learning for multi-domain ABSA tasks, achieving significant performance improvements.
Magahi Code-mixed Dataset for Sentiment Analysis: Linguistic Analysis and Model Evaluation
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。
Extensible Multi-Granularity Fusion Network for Aspect-based Sentiment Analysis
The Extensible Multi-Granularity Fusion (EMGF) network integrates diverse linguistic and structural features efficiently, resulting in superior performance in Aspect-Based Sentiment Analysis (ABSA).
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