Hierarchical Attention and Contextual Embedding for Robust Sarcasm Detection

Abstract

Objective: The study aims to develop a technique for sarcasm detection in social media text to enrich the sentiment analysis. Methods: The methodology used in the present research is as follows: emotion-embedded vectors for capturing the emotional content of the text, dynamic contextual modulation for the adaption of the model to the given context, and Hierarchical attention mechanism for segmentation of the text at the different level of abstraction. Findings: Evaluation with the test set proved that the proposed model achieved accuracy rates 89% higher than the benchmark models. Including these several complicated methods helped achieve greater accuracy and F1 measures, contributing to sarcasm detection efficiency. Novelty: This approach ensures that vital sarcastic alerts are detected, providing a better text analysis and increasing sentiment analysis's accuracy and robustness. Keywords: Sarcasm detection, Sentiment analysis, Natural language processing, Deep learning, Opinion mining