Sarcasm detection of tweets without #sarcasm: data science approach

Identifying sarcasm present in the text could be a challenging work. In sarcasm, negative word can flip polarity of positive sentence. Sentences classified as sarcastic or non-sarcastic. It is easier to identify using facial expression tonal weight rather detecting from plain text. Thus, detection natural language processing major challenge without giving away any specific context clue such #sarcasm tweet. Therefore, research tries solve this classification problem various optimized models. Proposed model, analyzes whether given tweet, not presnece hashtag kind To achieve better results, we used different machine learning methodology along with deep embedding techniques. Our model uses stacking technique which combines result logistic regression and long short-term memory (LSTM) recurrent neural net feed light gradient boosting generates compare existing network algorithm. The key difference our work done has been much explored earlier by researcher. metrics for evolutionis F1-score confusion matrix.