Paper Title
Innovative Frameworks for Sarcasm Detection: Challenges and Multimodal Trends

Abstract
Detecting sarcasm is a difficult task in opinion analysis due to its subtle nature and dependence on context and diverse information sources. This paper presents a detailed review of methods used for sarcasm detection, focusing on both text-based techniques and those using multiple types of data. It covers modern approaches like attention mechanisms, deep learning, and multitask learning, along with traditional techniques. Recent research highlights the importance of sentiment analysis and combining multiple types of data to improve sarcasm detection in various scenarios. The paper also discusses the impact of contextual embeddings and attention mechanisms, which have boosted detection accuracy. Furthermore, it explores how cultural and language differences affect sarcasm detection, with a focus on challenges in multilingual and cross-lingual contexts. Emerging trends, such as hybrid models and combining data from different sources, are also reviewed, showing potential for better performance and flexibility in sarcasm detection systems. Finally, the paper identifies areas for future research, emphasizing the need to integrate diverse types of information and contextual signals. This comprehensive approach aims to handle the complexity and variability of sarcastic expressions across different platforms and languages, leading to better understanding and detection of sarcasm in varied settings. Keywords - Natural Language Processing, Deep Learning, Opinion Mining, Multimodal Sarcasm Detection, Contextual Embeddings.