[LREC-COLING'24] Source code for the paper "When Do More Contexts Help with Sarcasm Recognition?"
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Updated
Mar 21, 2024 - Python
[LREC-COLING'24] Source code for the paper "When Do More Contexts Help with Sarcasm Recognition?"
An NLP project on detecting sarcasm in texts which don't contain any non-offensive term but are sarcastic in the context of the conversation.
Sklearn, logistic regression, Naive Bayes classifier, K-Nearest Neighbors, decision trees
Advanced NLP project where we needed to build a Sarcasm/Irony classifier. It has many methods like BiLSTM, Transformers to BERT/ T5 / MPNET finetuning
Sarcasm detection on tweets using neural network
Sarcasm dataset, 15K tweets, very high quality, both intended & perceived sarcasm, rich context
[NeurIPS 2022 Oral (Spotlight)] Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification
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.
Sarcasm detection model, trained on Sarcasm on Reddit Dataset.
A neural network trained for detecting sarcasm in reddit comments. This project was implemented in python using jupyter notebook for the applied machine learning algorithm course at KIT. Everything was developed together with Philip Schröder as a two man project.
This repo contains code to detect sarcasm from text in discussion forum using deep learning
Sarcasm detection in textual data using different feature embeddings and models.
This is an NLP project, where I am attempting to detect sarcasm in social networks in Persian language.
Detecting sarcasm in texts
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 …
A project consisting of analysis of sarcasm in text using Natural Language Processing techniques. It highlights the importance of context and punctuation in sarcasm detection. Different deep learning models are applied and compared to get the best accuracy in sarcasm detection.
It is the implementation of paper "Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model"
Sarcasm Detection using LSTM, GRU, and RoBERTa on SARC (reddit), sarcasm_v2, and iSARCASM (twitter) datasets
Multimodal Sarcasm Detection Dataset
Official resource of the paper "Sentiment Analysis and Sarcasm Detection using Deep Multi-task Learning", Wireless Personal Communications Journal 2023
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