TY - JOUR
AU - Leelavathi, R.
AB - The challenge of sarcasm detection in social media is heightened by its informal vernacular, the necessity for context, and the nuanced linguistic indicators that define sarcasm. This study explores the effectiveness of bi-LSTM models in conjunction with GLOVE and word2vec embeddings in identifying sarcastic expressions within social media platforms. This empirical evidence suggests that bi-LSTM models equipped with GLOVE embeddings surpass those with word2vec embeddings in all performance metrics, including accuracy, precision, recall, and F1-score. The bi-LSTM models demonstrate an improved ability to discern sarcasm's subtleties in text thanks to GLOVE's ability to encapsulate semantic meanings and contextual nuances. This research underscores the critical role of advanced pre-trained embeddings like GLOVE in elevating NLP tools for sarcasm detection on social media. Such advancements are particularly relevant to fields that depend on precise interpretation of user sentiment, such as brand monitoring, sentiment analysis, and broader social media analytics.
TI - Sarcasm detection using enhanced glove and bi-LSTM model based on deep learning techniques
JF - International Journal of Intelligent Engineering Informatics
DO - 10.1504/ijiei.2025.144268
DA - 2025-01-01
UR - https://www.deepdyve.com/lp/inderscience-publishers/sarcasm-detection-using-enhanced-glove-and-bi-lstm-model-based-on-deep-Q72aohnleX
SP - 26
EP - 54
VL - 13
IS - 1
DP - DeepDyve
ER -