Sentiment analysis and emotion detection are important research topics in natural language
processing (NLP) and benefit many downstream tasks. With the widespread application of …

The global outbreak of COVID-19 has had a substantial impact on various sectors
worldwide, including the economy, healthcare, entertainment, policy formulation, and …

In today's global digital landscape, misinformation transcends linguistic boundaries, posing
a significant challenge for moderation systems. While significant advances have been made …

Creating systems that can interpret and manage the ambiguity and subjectivity of the
representation and retrieval of information is one of the issues facing information systems …

The internet has brought both benefits and harms to society. A prime example of the latter is
misinformation, including conspiracy theories, which flood the web. Recent advances in …

Sentiment analysis and emotion detection are critical research areas in natural language
processing (NLP), offering benefits to numerous downstream tasks. Despite the widespread …

Misinformation is prevalent in various fields such as education, politics, health, etc., causing
significant harm to society. However, current methods for cross-domain misinformation …

Emotion Recognition in Conversations (ERCs) is a vital area within multimodal interaction
research, dedicated to accurately identifying and classifying the emotions expressed by …

Detecting fake news in large datasets is challenging due to its diversity and complexity, with
traditional approaches often focusing on textual features while underutilizing semantic and …

Abstract Multimodal Aspect-Based Sentiment Analysis (MABSA) technology aims to utilize
both textual and visual modalities to achieve Multimodal Aspect Term Extraction (MATE) and …