A systematic literature review on enhancing sarcasm detection in online reviews through emotional transition analysis and contextual embedding techniques.

Analysing online reviews is crucial for understanding user sentiments, yet sarcasm often hinders accurate sentiment evaluation. In this systematic literature review (SLR), we addressed the challenging task of sarcasm detection in online reviews, focusing on two key aspects: emotional transitions within reviews and the capabilities of contextual embedding techniques. Due to the context-sensitive and multifaceted nature of sarcasm, most traditional natural language processing (NLP) systems struggle to detect sentiment accurately. To identify sentiment shifts indicative of sarcasm, this systematic literature review (SLR) examines existing literature on emotion transitions within review texts. This review aims to illuminate the linguistic cues associated with sarcastic sentiment reversals. In order to identify these cues more effectively, we further explore the application of advanced contextual embedding models, such as BERT and ELMo, for their ability to detect and analyse sarcastic expressions. By synthesising these findings, we offer a comprehensive overview of current approaches to sarcasm detection in sentiment analysis. Additionally, this review critically analyses the limitations of existing methods and proposes potential avenues for improvement. We aim to explicate the effectiveness of sarcasm detection in sentiment analysis applications used on social media platforms like X (Twitter), Reddit, Facebook, and WeChat. In brief, this review provides valuable insights into the field of sentiment analysis and emotion detection, paving the way for the development of NLP systems with a refined ability to discern intricate emotional and contextual embedding in online reviews.