NLP-Based Extended Lexicon Model For Sarcasm Detection With Tweets And Emojis
M Sreenandan Reddy, Anitha Bukkacherla, Chowdeshwari Pujari, Baba Fareed Shaik, Neeraja Narla, Dileep Kumar Gosala
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.
