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A sarcasm detection model using Bidirectional Encoder Representations for Transformers (BERT) and Graph Convolutional Networks (GCN) has shown state-of-art results against conventional models and vanilla transformer-based approaches.
An NLP based APP includes features for spam detection, sentiment analysis, stress detection, hate and offensive content detection, and sarcasm detection. It leverages Natural Language Processing (NLP) techniques and machine learning models to analyze and classify text inputs. Table of Contents
Sarcasm is a term that refers to the use of words to mock, irritate, or amuse someone. It is commonly used on social media. The metaphorical and creative nature of sarcasm presents a significant difficulty for sentiment analysis systems based on affective computing. The technique and results of our team, UTNLP, in the SemEval-2022 shared task 6 …
The goal of this project is to identify sarcasm in plain text.the project plans to exploit the property of a general sarcastic statement of possessing contrasting sentiments by using Natural Language Processing.The project aims at training a machine learning model using TensorFlow to detect if a given statement is a sarcastic or regular sentence.
This is a Text Analysis App which can be used to find a detailed analysis of a particular text. This includes 5 main types of Analysis - Spam/Ham Detection, Sentiment Analysis, Stress Detection, Hate & Offensive Content Detection, Sarcasm Detection