NLP-Based Extended Lexicon Model For Sarcasm Detection With Tweets And Emojis

Sarcasm detection in social media, especially on platforms like Twitter, poses a significant challenge due to the informal and context-dependent nature of language. This research presents an NLP-based extended lexicon model for sarcasm detection, leveraging both textual features and emojis to enhance interpretability and accuracy. The proposed model integrates a sentiment lexicon with syntactic and semantic cues, enriched by emoji sentiment analysis, to capture subtle contradictions and ironic tones in tweets. A dataset of labeled tweets is preprocessed and analyzed using natural language processing techniques, including tokenization, POS tagging, and feature extraction. Machine learning classifiers are then employed to detect sarcasm. Experimental results demonstrate that the inclusion of emoji sentiment and lexicon-based features significantly improves detection performance, highlighting the importance of multimodal analysis in understanding sarcasm in social media texts.