Sarcasm Detection in Tweets with BERT and GloVe Embeddings
arXiv:2006.11512v1998 citations
Originality Synthesis-oriented
AI Analysis
This addresses sarcasm detection for social media analysis, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled sarcasm detection in tweets by using BERT and GloVe embeddings with context from user reactions, achieving unspecified results without concrete numbers.
Sarcasm is a form of communication in whichthe person states opposite of what he actually means. It is ambiguous in nature. In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets. The dataset is preprocessed before extracting the embeddings. The proposed model also uses the context in which the user is reacting to along with his actual response.